Application of Multi-Agent Systems to Shared Transport Services: a review

Jadna Cruz, Elis Silva, Rosaldo J. F. Rossetti, Daniel Castro Silva, Eugénio C. Oliveira, João Neto Laboratório de Inteligência Artificial e Ciência de Computadores Departamento de Engenharia Informática Faculdade de Engenharia da Universidade do Porto Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal {up201701159, up201409733, rossetti, dcs, eco, jneto}@fe.up.pt

Abstract — Several studies have extended the application of whether only prototypes or simulation studies were Multi-Agent Systems (MAS) to a wide range of different implemented when no evidence of real applications is found. domains, including shared services with various purposes. With regard to transport systems, shared services enabled through The review consists in evaluating and interpreting studies in technological solutions, especially in mobile space, have many the area of interest [5], so as to answer the following research benefits, such as mitigating traffic congestion and reducing questions: greenhouse gas emissions, among others. In addition, with the population of large cities rising more than ever, citizens are x How are MAS addressed in shared transport services? becoming more sensitive to societal issues and other performance x What are the limitations of implementing MAS in measures imposed by the new concept of smart cities. This work shared transport services? will study the literature with the objective of verifying the proposals and limitations of existing studies, prototypes and x What are the challenges for the deployment of these simulations, in order to provide a systematic evidence-based view systems in the real world? of how the scientific community is applying MAS to leverage shared services for the implementation and feasibility of x How is the performance of shared transport services intelligent mobility solutions. In the course of the studies, we assessed? conclude that although some authors came to propose conceptual models and systems prototypes, the simulation component has II. PRELIMINARY CONCEPTS been little explored and only synthetic data has been used. One of Before proceeding with the study of the literature, it is the reasons justified by the authors relates to the high costs of necessary to introduce the two main concepts involved, namely carrying out simulations. We did not find in the literature Multi-Agent Systems and Shared Transport Services. This proposals that were effectively implemented and tested in the real section briefly summarises both concepts. world, but only prototypes and proposals of future developments. According to Russell et al. [6] an agent is able to perceive Keywords - Multi-agent Systems; Shared Services; Mobility; its environment by means of sensors, and to act upon this same Systematic Review. environment through actuators. For Ivamoto et al. [7] multi- agent systems are systems in which several agents work I. INTRODUCTION together and have their own existence, independent of the Population growth and the continued use of cars in urban existence of other agents, have a set of behavioural capacities areas are bringing about severe problems for large cities, such that define their competence, objectives and autonomy. Each as congestion, air pollution or fuel waste, among others [1][2]. agent can have an individual purpose and, collectively, agents Cars account for approximately 70% of all greenhouse gas can cooperate and collaborate in improving the performance of emissions and are now the dominant form of transit with the system. private passenger cars, which are often occupied by one single For Leitão et al. [8], multi-agent systems suggest the passenger [3]. These problems have great impact on people’s definition of distributed control based on cooperative and lifestyle, the environment, and the economy of cities, and must autonomous agents that work co-ordinately to perform a task. be tackled appropriately by policy makers. The distributed nature of MAS allows a significant amount of Shared transportation services have been used as data to be processed, due to the scalability of these systems. alternatives to conventional means of transportation, so as to Multi-agent systems can be extended by adding new agents or minimize the problems described above [4]. Based on the new behaviours, thus being appropriate to the context of aforementioned context, this work proposes a systematic decentralized and heterogeneous environments, where large review of the literature to address relevant studies on changes can occur [9]. Due to their very characteristics, MAS applications of multi-agent systems to shared transport have been largely used to cope with the so-called DDD services. This paper contributes with a showcase of proposals systems, which are highly dynamic, mostly distributed, and in that already exist in the literature, emphasising on their which decisions a generally made on a decentralised basis, by objectives, main achievements, and limitations, as well as on multiple actors endowed with different capabilities and playing A. Protocol different roles. We defined the research theme based on the new trends of As for the concept of shared transport, Carpooling, solutions in the emerging fields of Smart Mobility, seeking to sometimes also referred to as Ridesharing, represents a solution bring contributions to the literature in the area of study. Thus, that allows private cars to become part of the public transport the main components of the methodology are the following: system, benefiting users and the environment [10]. For Correia 1) Definition of the theme: “Application of Multiagent et al. [11] carpooling is considered a Transport Demand Systems to Shared Transport Services”. This study topic Management (TDM) tool that produces a decrease in the number of single-occupant vehicle journeys, especially when investigates two major areas, namely Smart Mobility and people are from the same company, or share common places. Multi-Agent Systems. For Pukhovskiy et al. [3] carpooling is a dynamic system, 2) Reviewers: It was established that two reviewers would based on two sources of information, namely members and the participate in the review protocol, both in the search process system, and quickly and easily disseminates members’ of articles, and in the process of article inclusion / exclusion. itineraries, creating flexible and intelligent routes through the 3) Objective: Analysis of several approaches on system (server end). multiagent systems in shared transport. It attempted to Carpooling services have been around since the mid-1970s perceive the existing gaps in the literature, a process also and are now available on websites or online applications where known as gap analysis. members can post carpooling requests and obtain matching 4) Research questions: as identified in Section I. offers. Members are provided with the coordinates of other 5) Definition of bibliographic databases: The most well- users so that they can contact each other directly and share known digital databases and libraries, namely IEEE Xplore, travel details (i.e. meeting point, time, etc.) [12]. Other systems Springer, Scopus, ACM Digital Library, Science Direct, and allow members to connect online through chat rooms to Google Scholar are used. address points of interest in the trip. Models of online static 6) Definition of the search strings: Terms of reference systems are widely used and allow users to plan their travels in combining keywords, such as ((Carpooing OR Ridesharing advance. With the emergence of new technologies, many travel-sharing service proposals have been integrating the OR Smart Transport) AND Multi-agent systems) were concept of multi-agent systems [13][14] with the aim of considered. making them more dynamic and efficient. 7) Review and evaluation of the studies: The method for inclusion considered attributes such as title, abstract, In the literature, studies were carried out on solutions to keywords in the field of study, and full review of the article, find the best trajectories to be shared by passengers and drivers, with similar origins and destinations, using applications based whereas the method for exclusion considered articles that on multi-agent systems for the calculation of routes [15]. were not related to the search criteria or that did not meet the Proposals for service models have been studied in order to inclusion criteria. implement them in large urban areas, as suggested by Sirisena 8) Study Summary: The research carried out resulted in et al. [16], Sghaier et al. [17], and Bandara et al. [18], among the writing of this article, with a summary of the information others. that addresses the research questions defined in the systematic review. Replicability of the studies was also commented. III. METHODOLOGICAL APPROACH 9) Presentation of the results: the final results are This work will initially focus on the first phase of a described from a comparative perspective, as presented in systematic review of the literature, namely the phase of Table I that highlights the main characteristics of the articles mapping or identification of related work addressing the analysed. subject under study, as well as its preliminary analysis. This process will provide an overview of the study area, the types of related studies, and their evolution over time [19]. A systematic review of the literature is a methodology for the production of a bibliographic review, through defined and structured steps that provide reliability and theoretical basis [20]. Our point of interest is a review of the literature on applications of multi-agent systems to shared transport services. Therefore, we will analyse bibliographic studies of relevance in this area of research, and verify technical and methodological approaches used in the proposed services. From the studies analysed, we synthesise the information that answers the research questions herein proposed. We will also try to identify gaps in the literature still remaining unbridged, which may represent opportunities for the development of innovative solutions. Figure 1. Articles analysed in the study. The systematic review began on October 24, 2017, with assist the traveller in planning the trips [25], combining public completion on January 5, 2018. A total of 111 articles were and individual transport. Tasks were run in parallel, from order collected. However, in the first classification phase, in which acquisition to transferring solution to users. The system was only titles, keywords, and abstracts of articles were verified, composed of three layers called the DyCOS (Dynamic only 61 of those were considered for the following phase, Carpooling Optimization System). since they corresponded to the domain of study. In the second In Bonhomme et al. [9] authors report that one of the main phase, a more detailed reading was carried out, and it was carefully checked whether they were in accordance with the problems with carpooling is the existence of a prior agreement proposed inclusion criteria. Afterwards, only 25 articles were between the driver and the potential passengers. In this work, a included in the survey, and the remaining 36 articles were solution based on a multi-agent platform was proposed in the context of the WiSafeCar (Wireless Traffic Safety Network excluded. From Figure 1, it can be seen that only a minority of the articles were related to the topic of study. Just a few between Cars) project, focusing mainly on security services that allow the mutual authentication of users and application studies seem to consider the use of multi-agent systems for solutions in shared transport, which suggests it is an area still components with the system. However, in a more recent study by Armendariz et al. [15], a prototype of the WiSafeCar project little explored, and therefore can be a fertile ground for new ideas and solutions to be tested and devised. was simulated in conjunction with the NetLogo framework that creates a dynamic cycle system that optimises the use of IV. LITERATURE ANALYSIS transport by sharing travel between people who normally use the same route, or common parts thereof. As previously described, several papers address solutions to find better shared routes using MAS; however, just a few apply A study by Kashyap et al. [26] proposed a dynamic MAS to the specific domain of shared mobility. In the study by carpooling application for the Android mobile operating Sirisena et al. [16], a system is proposed taking into account the system using MAS. The architecture was composed of three time and the desired path. The system has three layers, where layers: the user interface layer, responsible for the passenger the first layer consists of the passengers, drivers and the information record; the application layer, which combined the combination of routes, the second corresponds to the database and agent layer authentication; and finally the communication between the agents, and a set of ontologies is ontology layer, responsible for the database and system rules. placed in the third. In this proposal, authors identified problems The main objective of the study was the safety of passengers, related to the flexibility and security of the system. especially female passengers. A set of rules and instructions was defined in the system, such as the decision of a female In the work carried out by Sghaier et al. [21][17], authors driver to refuse the trip if there were only male passengers, or if approached the problem of dynamic request processing a female passenger refused a trip with a male driver. The optimization and its complexity. They propose a new approach proposed application was not simulated and did not yield called DOMARTiC’s (Distributed Optimized Approach based reported results. on the Multi-Agent concept Real Time Carpooling services), using graphical modelling and the multi-agent concept to In Hussain et al. [27][28] a carpooling system using agents decompose the Optimized Dynamic Carpooling Problem was proposed. The activities of the agents were composed in (ODCP) into multiple less complex tasks, and generate three phases: social networking, negotiation between agents, optimized responses within a reasonable execution time. and travel execution. They used synthetic data and the Janus Traceability, communication and security of the service were platform in the simulated experiments. They evaluated the key characteristics identified by the authors. In another study evolution of a carpooling society, its interactions, adaptations by Sghaier et al. [22], a proposal of DARTiC is approached and behaviours, validating the study with real data collected in using the distributed Dijkstra algorithm for the implementation Flanders, Belgium. In the negotiation phase, agents adapted of a real-time carpooling system based on the concept of multi- their daily schedules, cooperating with the switching process. agent systems. The objective is to find the best solution in a They used 30,000 individual agents from different zones in the reasonable or approximate response time, considering the simulation runs, and they could communicate with five agents restrictions (route matching, vacant places, etc.), and composed in 145 days of simulation. When the simulation reached 30 of several cars. The approach in Sghaier et al. [23], Optimized days, there was an increase in “ride” groups; it was easier for Real Time Carpooling (ORTiC) was proposed with the use of an agent to join an existing “ride” group than to create a new MAS in the decomposition of parallel requests, locating “ride.” In 45 days, there was again a gradual increase in the available cars in defined geographic areas, and processing user number of “carpool.” It was observed that when the time requests based on requirements, such as expenses, duration of window is larger, the chances of negotiation between agents the trip, among others. are greater, and when there are restrictive activities, the chances of success in negotiation between agents are lower. In Cheikh et al. [24] a dynamic Multi-Hop Ridesharing Service (M-HRP) was proposed for calculations of routes and In another study by Hussain et al. [29], a simulation of a similar schedules between passengers and drivers, and the population of heterogeneous agents was carried out, involving search order. A decentralized architecture was proposed, where learning, interactions, adaptation and organisation. Their work problems were broken down into multiple sub-problems. They provided organisational concepts to build the agent-based also used multi-agent systems combined with the M-HRP, simulation model. The approach is appropriate in dynamic resulting in evolutionary algorithms based on agents. In settings, when agents change their behaviour dynamically, another study the authors describe the multi-agent system to without changing their internal architecture. The goal is to generalise the concept of multi-zone interaction in the social concluding that 2/3 of the population were driving, and the network of the carpooling system. In the simulation, the data remaining 1/3 were passengers; (ii) The distribution between were used from the previous studies, so as to analyse the modes of transport, concluding that 65% of people travelled in effects of interaction, adaptation, decision-making, individual vehicles, 31% used public transport, and only 4% communication, negotiation and coordination of agents in used “rides,” with an average number of two people per multiple trips. In this study, they concluded that it is possible carpooling group; iii) average computational time in the for agents to adapt their schedules so as to favour cooperation. simulations, concluding that the simulator needs to be improved on the quality of simulation results and performance. In Cho et al. [30] an agent-based conceptual model for a carpooling application has been proposed. They simulated the In Knapen et al. [35], the authors addressed scalability interactions of autonomous agents, behaviour, and cost. They issues in global matching carpooling service using MAS. The used data from agent profiles and social networks. Route service determined the corresponding travels, and negotiation scaling algorithms were used. They used a collection between agents. The consulting agent learned parameter values procedure, which created a motive for sharing and in preparing the advice. Periodic trips were recorded by agents, communicating among the other agents, negotiating a route and thus were estimated the probability of a negotiation of a plan and executing them. Feedbacks were provided to the pair of travel being successful. The model used activity data agents. The University of Hasselt in Belgium, with the from the FEATHERS model, with a daily schedule of members collaboration of the Transport Research Institute (IMOB), of a synthetic population. Predictions and construction of a provided timeline and attribute data through an activity-based network with the agendas of the synthetic population were traffic demand model, coined FEATHERS. In another study by carried out. Time and route were relevant factors in the success Cho et al. [31], ontology techniques were used in the agent- of negotiation between agents; however, there were no based model. Three ontology functions were implemented, feedbacks on accessible negotiations. namely for the integration between databases, compatibility The study developed by Bandara et al. [18] proposes a and consistency between models, and for the communication of agents. These techniques were used in cohesion simulation to ridesharing system using MAS for instant correspondence verify their capacity in the activity-based micro simulation between drivers and passengers. The use of mobile devices is necessary in the communication between users and providers. research. The system consists of drivers, passengers and the service In Bellemans et al. [32], the challenges of constructing provider. Through instant sharing, driver’s agents stop traveller behavioural models in shared transport systems using competing for specific passenger orders, when his/her car GSM, GPS and Bluetooth data are described. Authors analysed becomes completely busy, withdrawing its initial offer of the factors that influence the choice of “ride,” as well as the “ride.” All driver agents scan the message space constantly for proposal of the agent-based simulation model. The initial other requests and continue the previously described process. sharing system proposal was to meet the transportation demand When matches are not found, users are notified of the most of people who worked in large factories. The negotiation relevant rides, giving them the option of choosing any. between agents is an important factor for the good functioning However, if the driver chooses an offer, there is a possibility of the system, as well as for the routing and scheduling of that it will be rejected by the passengers. passengers. A synthetic population of agents was created, with socio-demographic characteristics, mobility and social In the analysed studies, authors did not consider any relations, similar to a real population. The performance of the appropriate methodology for agents, which could facilitate a system would be measured through the delay induced by the better planning of activities. Many solutions seem to result from the intuition of the expert in charge of the modelling general congestion, and the number of kilometres traversed process. A study by Passos et al. [36] proposes an agent- versus the average occupancy of the vehicle. oriented methodology focused on complex scenarios using an In Galland et al. [33], authors used the conceptual model of approach based on the Gaia and Porto methodology. The the carpooling application proposed in [30]. The model business process modelling approach (BPM) was used to allowed for the selection of the best mode of transport based on model the complex behaviour of the environment in MAS characteristics, such as: maintain a social network, negotiate to applications. A multimodal travel planning system was used to share “rides,” and transport the driver and passengers of a car. exemplify the methodology. The user requests a route from the It also allowed for travel information to be periodically system, from its current point and its desired destination. The published for consultation by the users of the system. They multimodal glider choses the best travel plan; however, the analysed the effects of interaction, communication, negotiation user could either accept the suggested plan or not. and the ability to simulate agent learning. They used algorithms for route matching, and a utility function in negotiating V. ANALYSIS OF RESULTS between agents. In another study by Galland et al. [34], the This literature review sought to answer the three research conceptual model of the previous study was used, in which the questions presented in the Introduction. After analysing the authors carried out its design and implementation. They used included articles, we have come to the following conclusions: 1,000 agents in the simulations, with the purpose of calculating and optimizing the solution time in the interactions, and - How are MAS addressed in shared transport services? predicting the outcome of the negotiations between agents. At In answering this question, we mentioned some studies every four hours within the reference day, they evaluated that used MAS to solve specific problems, such as: process information about: i) the proportion of drivers and passengers, optimization, dynamic requests, task subdivision, real-time planning, in which cases such features were also considered as localisation, distributed processing, dynamic carpool important characteristics. assignment, and best time solution of response in carpooling systems, such as in Sghaier et at. [17], [21], [37], and [23]. In TABLE I. CHARACTERISTICS OF THE REVIEWED ARTICLES. the studies of Ckeikh et al. [24] and [25], authors addressed Conceptual Conceptual the dynamic ridesharing system and a MAS was proposed to Simulation Scheduling Prototype System Model Route solve the problem of passenger assignment to drivers, time Time calculation, passenger search and delivery order, route plans, Articles

time and cost, decentralised architecture, and problems compiled into multiple sub-problems, order management, message encryption, and public and individual transport travel planning. We mention only those studies that summarise Sirisina et al. [16] 9 9 9 existing MAS approaches in carpooling as well as their Sghaier et al. [21],[17] 9 9 9 9 Sghaier et al. [22],[23] 9 9 9 9 solutions prototypes. Cheikh et al. [24] 9 9 9 - What are the limitations in the implementation of MAS in Cheikh et al. [25] 9 9 9 shared transport services? And what are the challenges for the Bonhomme et al. [9] 9 9 9 Armendariz et al. [15] 9 9 deployment of these systems in the real world? Kashyap et al. [26] 9 We cite the studies of Bonhomme et al. [9] and Hussain et al. [27],[28] 9 9 9 9 Armendariz et al. [15] to answer this question. We note in Galland et al. [31],[32] 9 9 9 9 Cho et al. [29], [34] 9 9 9 9 their studies that the main limitations of MAS when applied to Knapen et al. [30] 9 9 shared transport is flexibility of services, system security, and Bellemans et al. [33] 9 9 9 the consideration of previous agreements of service for drivers Bandara et al. [18] 9 9 9 and passengers. In addition, we can also describe that in other studies, issues such as confidence in using the service, the VI. CONCLUSIONS real-time location module, as well as process optimization and In this work, we carried out a systematic review of the dynamic loads were addressed. As for challenges to their literature with the objective of analysing the relevant studies implementation in the real world, we emphasise on the that addressed multi-agent systems applied in shared transport necessity of a dynamic approach for the system. Carpooling solutions. The study sought to answer the proposed research services available on the Internet are static, with travel questions on how MAS are approached in carpooling, their schedules planned very much ahead of time by members of limitations for practical implementation, and evaluation of the the service platform. service. Based on the analysis, we can conclude that although - How is the performance of shared transport services some authors came up with different conceptual model evaluated? proposals and prototypes of systems, the simulation component was little explored, as only synthetic data was The studies that simulated the prototype of the system used considered. One of the reasons justified by the authors relates platforms such as Janus, NetLogo, and Jade, as in Hussain et to high costs of carrying out simulation studies of this sort. We al. [27] and [28]. In the studies of Cho et al. [30], authors did not find in the literature proposals that were actually validated their simulations using synthetic data and assessed implemented and tested in the real world, but only prototypes the travel journey and the flexibility of the system. They also and proposals for forthcoming developments. In future work evaluated routes between driver and passengers, and the we intend to propose a shared mobility platform resorting to execution time of the algorithms, the dynamic re-planning of the concept of multi-agent systems and appropriate routes, and the monetary cost of the trips. The feedback and mechanism design [38] so as to positively influence agent- classification of the service and the driver were also addressed based demand [39] [40]. Thus, following an appropriate agent- in the analysed studies. In real-world carpooling systems, oriented methodology is paramount, since none of the studies driver classifications are performed by passengers on online reviewed in our research followed such methodologies. We platforms. follow an approach already proposed, based on the semantic We highlight in Table I some characteristics that we deem constructors of both the Gaia and the Porto methodologies, important for this study. The first feature is whether or not the which can be found in Passos et al. [36]. In addition, we intend examples use MAS, for which we tried to understand if the to create a carpooling application for the academic community authors proposed conceptual agent models based on their of the Faculty of Engineering, at the University of Porto shared transport solution. On this specific aspect, we found out (FEUP), and hence evaluate both the system and the users on that most of the included studies addressed this characteristic. issues related to important characteristics of the service The second characteristic refers to proposals for carpooling provided, namely trust, safety, time spent, monetary cost, system prototypes. However, only a few studies have among other performance measures to be defined. addressed this feature. The third feature analysed is the use of simulation tools to evaluate the system. Finally, we have also REFERENCES taken into account that authors sought to solve some other [1] Y. Doytsher, P. Kelly, R. Khouri, R. McLAREN, and C. Potsiou, “Rapid specific problems for their carpooling systems, such as the urbanization and mega cities: The need for spatial information calculation of travel time of a trip, as well as the best route management,” FIG Report, no. 3, 2010. Copenhagen, Denmark: FIG. [2] R. Olszewski, P. Pałka, and A. 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