Types of Agents in Peer-to-Peer Shared Ride Systems

Yun Hui WuDept. of Geomatics

The University of MelbourneVictoria 3010, Australia

y.wu21@pgrad.unimelb.edu.au

Lin Jie GuanDept. of Geomatics

The University of MelbourneVictoria 3010, Australia

Stephan WinterDept. of Geomatics

The University of MelbourneVictoria 3010, Australia

ABSTRACTShared ride systems match the travel demand of transportclients with the supply by vehicles, or hosts, such that theclients find rides to their destinations. A peer-to-peer sharedride system allows clients to find rides in an ad-hoc manner,by negotiating directly with nearby hosts via radio-basedcommunication. Such a peer-to-peer shared ride system hasto deal with various types of hosts, such as private carsand mass transport vehicles. Their different behaviors affectthe negotiation process, and consequently the travel choices.In this paper, we present and discuss a model of a peer-to-peer shared ride system with different types of agents.The behavior of the model is investigated in a simulationof different communication and way-finding strategies. Wedemonstrate that different types of agents enrich the choicesof the clients, and lead to local solutions that are nearlyoptimal.

Categories and Subject DescriptorsI.6.4 [Simulation and Modelling]: Model Validation andAnalysis; H.3.3 [Information Storage and Retrieval]:Information Search and Retrieval

General TermsPerformance

1. INTRODUCTIONMovement of people in a city forms a complex system. Itincludes the street network and other ways of travelling,traffic rules, traffic infrastructure (e.g., traffic lights, signs)as well as the cognition, decisions and actions of intelligent,autonomous agents such as pedestrians and vehicle drivers.This complex system is burdened by more and more trafficand expanding cities. In this situation a peer-to-peer sharedride system can contribute relief to the critical situation: itenables people to negotiate in an ad-hoc manner for ridesharing, and thus, helps reducing the traffic, increases ur-ban access, and improves the integration of different modes

of transport. In such a system, pedestrian are the agentswith transport demand, called clients, and vehicles, or hosts,provide the transport supply. Finding rides in an ad-hocmanner is accomplished by local negotiation between theseagents via radio-based communication.

A peer-to-peer shared ride system has to deal with varioustypes of agents, such as private cars and mass transport ve-hicles, or mobile and immobile clients, to cope adequatelywith the complexity of urban movements. The agents’ differ-ent interests, capacities and behaviors affect the negotiationprocess, and consequently, the trips made. For example,hosts can be distinguished by their travel speed, their pas-senger capacity and their fare structure, and clients can bedistinguished by their mobility.

In this situation a client cannot stay with a simple prefer-ence for one mode of travelling, i.e., one type of hosts. Forexample, in general a rushed client would prefer hosts candeliver a quick and direct trip: taxis. On the other hand,taxis can be in high demand during peak travel times andcatching trams, trains or buses can be an alternative: theymay travel slower but might reach the destination earlierdepending on traffic. Hence, in this paper we present anddiscuss a model of a peer-to-peer shared ride system withdifferent types of agents.

Agents, i.e., clients and hosts in peer-to-peer shared-ridesystems have knowledge of their environment. They cancollect and transmit information from/to their neighbors.Frequently agents have choices. They have preferences, var-ious optimization criteria, such as money or time, and areable to make current optimal decisions based on their knowl-edge. However, for practical reasons agents have only localand current knowledge of their environment. Previous re-search [18] investigates the ability to make trip plans fromdifferent levels of local knowledge. It shows that a mid-rangecommunication depth is both efficient (leading to less com-munication messages than for complete current knowledge)and effective (leading to travel time comparable to com-plete current knowledge). This investigation was based on asimulation with homogeneous hosts and an immobile client.The hypothesis of this paper is that involving other typesof agents, the trips will change significantly, but mid-rangecommunication is still both efficient and effective comparedto other communication strategies.

This hypothesis will be approached by simulation. The

simulation is realized as a multi-agent system, allowing usto model and understand individual behavior of differentagents. The approach requires identifying and specifying theessential aspects of an urban shared ride system, implement-ing them in a multi-agent system, and then running largenumbers of random experiments to generate the requiredevidence. The model can be investigated by systematicallyvarying the design parameters and studying the peer-to-peershared ride system behavior.

The structure of this paper is follows. Section 2 reviews pre-vious and related research. Section 3 discusses the types ofagents in shared ride systems. Section 4 presents the designof a multi-agent simulation, and the simulation results areprovided in Section 5. These results are discussed in Section6, and Section 7 concludes the research.

2. LITERATURE REVIEWThis review consists of an overview of shared ride systems ingeneral, specifically previous research on peer-to-peer sharedride systems in particular, this is followed by an overview ofagent-based transportation simulation, the approach used inthis paper. Algorithms for trip planning in dynamic envi-ronments, are also reviewed.

2.1 Shared ride systemsShared ride systems exist in many forms and names, such asdial-a-ride, car pooling, van pooling, or find-a-ride. Sharedride systems also have various levels of technological sup-port, such as being based simply on social convention, orusing a centralized database with pre-registration and/orpre-booking via a Web interface. Of interest in our con-text are the types of agents involved in these services, and aparticular extension of the ride sharing idea to peer-to-peerservices for ad-hoc ride sharing.

Van pooling can be seen as a prearranged shared ride ser-vice between home and workplace to save up parking spaces[11]. Traditional van pooling services are organized by pri-vate companies and are not door-to-door. People with regu-lar commuting schedules usually appoint together at a place.Vans run on prearranged times and routes according to re-quests. The drivers do not receive a fee while users arecharged subscription cost directly to the companies. Vanpooling is limited by the provider’s service area and not vi-able for areas or individual origins or destinations that donot have the critical mass of people using the service. Newusers can only participate in existing van pooling routes,or they can create a new van pooling group together withothers.

Mass transportation planning systems provide trip plan-ning based on predefined schedules and routes of meanslike underground, trains, buses and trams. Being govern-ment funded or subsidized, the fares are typically lowerthan the costs of private means of transportation. In ad-dition to guaranteeing mobility and access for everybody,this should also encourage people to mitigate individual cartraffic. However, such a shared ride is restricted to fixed timeschedules and routes, which is less comfortable than manyprivate transportation alternatives. To better satisfy users,dial-a-ride systems have been initiated. Dial-a-ride systemscan offer more flexible and comfortable door-to-door rides,

chiefly by commercial vehicles and taxis [5]. To utilize thevehicles’ passenger capacity, drivers can pick up other pas-sengers before reaching the destination of the first customer.The authors implement a dynamic dial-a-ride system, whichcan re-optimize routes after picking up new customers dur-ing services. Therefore, this dynamic dial-a-ride system sup-ports a many-to-many service—customers have different de-partures and destinations—and does not need booking inadvance.

Google Ridefinder1 provides a real-time approach for indi-vidual users to find a ride in local areas. Users have widechoices from taxis, limousines and shuttles, which are con-tract companies of Google. The locations of vehicles in thisservice, observed by GPS and collected in a central database,are said to be less than 5 minutes old, which practicallymeans the locations are correct within 2− 3km. Currently,this service only works in a few metropolitan areas in theUnited States. Using Google Map, users can view the po-tential host vehicles by entering city names or addresses andcall selected service providers to request a ride. But becauseonly locations of these vehicles are provided in this interface,users do not know whether the shown host vehicles have freepassenger capacity for them unless calling. What is more,the dispatching of vehicles varies from city to city and com-pany to company, therefore the users might get alternativerides by dispatchers. This is the common disadvantage ofcentral management shared ride services, because communi-cation does not happen between users and drivers. Commu-nication is duplicated between users and dispatchers, anddrivers and dispatchers.

Other shared ride applications provide textual Web inter-faces to attract registrations of shared ride clients and hosts,such as Ride Now! 2, RidePro3 3, eRideShare4, or Mitfahrzen-trale5. The applications are intended to provide shared rideservices to the public, and are maintained by local andregional agencies with central databases. Mediated tripsare usually regional or national travels, but lesser so inner-urban travels. To request or offer a ride, users (clientsand hosts) need to provide their home addresses, cell phonenumber, email addresses and requested trip details. Thenthe databases match requests and offers immediately andfeedback a contact list of potential shared ride hosts orclients. The choice is left to the users who can email or calltheir selections. Agencies need high-powered workstations,database servers and internet connectivity to run such anapplication. Personal computers or mobile devices with In-ternet connectivity are necessary as data terminals for theusers. Such centralized services are restricted to pre-tripregistration. High-volume real-time data updating—such ascurrent vehicle locations, current travel plans, and currentseat capacities of large numbers of vehicles in urban traffic—is not possible through a centralized service.

In contrast, a peer-to-peer shared ride system [18] enablespeople to negotiate in an ad-hoc manner for ride sharing,

1http://labs.google.com/ridefinder.2http://www.ridenow.org3http://www.ridepro.net4http://www.erideshare.com5http://www.mitfahrzentrale.de

which is suited for inner-urban rides due to the instanta-neous provision. Finding rides in an ad-hoc manner is ac-complished by local negotiation between these agents viaradio-based communication, as applied in mobile geosensornetworks. Users of this service—clients as well as hosts—have to be equipped with mobile devices that form the nodesin such a network. Since such devices are already popularfor other purposes, the potential user base is much largerthan for traditional shared ride services. Fortunately, thelocal negotiation process allows for fully scalable services, incontrast to centralized shared ride services.

2.2 Agent-based transportation simulationSimulation is an accepted approach to investigate the be-haviour of complex systems before implementation. As traf-fic congestion becomes a world-wide problem, effective waysof modelling and predicting traffic flow have become a re-search focus [1]. Burmeister et al. [4] analyze the potential ofmulti-agent modelling in traffic and transportation systems,which naturally characterize “geographically and function-ally distributed” (p. 52) subsystems, such as traffic manage-ment, traffic guide and control, and capacity and resourcemanagement. Russel and Norvig [16] define an agent as“anything that can be viewed as perceiving its environmentthrough sensors and acting upon that environment throughactuators”, and describe agent behavior mathematically byfunctions.

Cellular automata (CA) are popularly applied in agent-basedmodelling. CA arrange individual automata in a cellularspace, where each cell has its state. Automata can collectinformation from their neighbors, and change their states ac-cording to their neighbors’ states and transition rules. Dueto its simple structure, CA have proven their success in landuse and urban planning. However, CA are inefficient in rep-resenting mobile agents, because cells themselves can notmove [3]. Additionally, state transition is too simple to im-plement the negotiation processes in shared ride systems.

Benenson and Torrens [3] combine CA and multi-agent sys-tem concepts and extend them as geographic automata sys-tems. Geographic automata systems are multi-agent sys-tems in which agents are distributed in space, allow au-tonomous behavior, in particular to re-locate, and interactwith each other. States of geographic automata, includingtheir location, are influenced by their neighbors, and thebehavior of automata is specified by state transition rules.

In transportation systems, pedestrians, vehicle drivers, traf-fic controllers, traffic lights and toll collectors are all agents.The main challenge of modelling and predicting in transportsystems is human behavior, which can be unpredictable.

Nagel discusses that travellers always know where they areheading, and the strategies in simulation decide in whichmode they move (walk, bus, car, bicycle and so on) andhow [12]. Nagel and Marchal introduce computational tech-niques for multi-agent simulations in this domain [13]. Mod-elling issues include strategy generation, adaptation, learn-ing and feedback. Each agent can perform multiple strate-gies and memorize them. After comparing the outcomeof different strategies, agents can choose a previously triedstrategy. The authors also address that the advantages of

agent-based modelling could be unclear in practice, due tothe complexity of real world processes.

Object-oriented languages, such as C++ and Java, are sug-gested for such agent-based modelling. Several establishedagent-based simulation libraries exist that simplify mod-elling. Swarm6 is one of the popular libraries based onObjective C and has a Java wrapper. RePast7 is a newerSwarm-like conceptual toolkit [14]. Repast is a free opensource toolkit core in Java, while it has three implementa-tions in Java, .Net and Python. Both approaches supportto program multi-agent systems that are composed of largernumbers of agents with functions describing their behavior.RePast was used successfully for a large-scale peer-to-peershared ride system simulation [17]. However, installing andusing libraries is in itself a larger effort, and we decided todevelop our system from scratch.

Object-Based Environment for Urban Simulation, OBEUS8,has been developed as a simplest implementation of geo-graphic automata systems in .Net [2, 3]. It is designed forurban processes and built in a cellular automata model withtransition rules in form of functions. Entities in OBEUScan be one of two types, mobile and immobile entities. InOBEUS no direct relationship is allowed between non-fixedobjects. That means that OBEUS is not suitable for oursimulation of locally communicating mobile agents.

2.3 Wayfinding algorithmsFinding shortest paths, in terms of some cost criteria, isthe key to shared ride planning. Clients are assumed tominimize travel costs in terms of criteria such as distance,time or money. The most widely known one-to-one shortestpath search is A* [9, 16]. The basic idea of A* is calculatingthe costs f for each intermediate node by the sum of g, thecosts from start to the node, and h, an estimate of the costsfrom the node to the destination. The estimate h can bedetermined by various heuristics, which are called admissibleas along as h ≤ c.

In a peer-to-peer shared ride system the shortest route isnot determined once, but regularly revised, based on the ac-tual local knowledge at different times and locations in thedynamic transportation network [18]. Hence, A* can be ap-plied in each trip planning process, and can be admissible ineach process for the actual local knowledge, but admissibilitycannot be preserved throughout consecutive trip plans withtheir different knowledge of the transportation network. Inthese dynamic environments life-long planning can be ap-plied [10, 20]. This is an adaptive A* shortest path searchwith a dynamic start point. Compared to static A*, lifelongplanning A* achieves less visited nodes and reduces the com-putational cost when weights are updated within network.Their approach deals with updates that increase previouslystored edge costs, whereas in our peer-to-peer shared ridesystem updates can decrease costs as well. Consider theworst case in shared ride systems is that clients have to walkfrom origin to destination, finding no ride. In this case, any

6http://www.swarm.org7http://repast.sourceforge.net8OBEUS can be downloaded from http://www.geosimulationbook.com

ride from a host found during a trip will reduce their triptime.

Time geography provides tools to improve the efficiency ofcommunication in the negotiation processes of the peer-to-peer shared ride system [19, 17]. Time geography is built onthe space-time paths representing the movement of individ-uals in geographic space over time [7]. The space-time prismis an extension, defined by the reachable areas between startand destination of a trip. The interior of the prism is calledpotential path space. Dynamic space-time prisms can beachieved, demonstrating that travel times between locationsvary by different spaces and times with dynamic traffic flow[21].

3. AGENTS IN SHARED RIDE SYSTEMSFor a peer-to-peer shared ride system it is essential to studythe nature of its peer users, both clients and hosts, beforetheir intentions, desires and beliefs can be modelled in a sim-ulation. For that purpose, only the factors that are criticalfor the aim of the simulation need to be identified. In thisresearch, the critical factors are the constraints on mobilityand the passenger capacity.

In order to reflect better the properties of realistic sharedride systems, we identify three typical kinds of hosts anddiscuss their distinct economic and operational characteris-tics in this section. They are mass transport, taxi cabs andprivate cars. We start by characterizing different types ofclients.

3.1 ClientsClients have a desire to travel to their destinations and de-pend on rides from hosts. We distinguish immobile andmobile clients. Immobile clients rely completely on rides.Mobile clients can alternatively walk forward by their own,but far slower than taking rides. Mobility of clients can de-pend on their preference, their luggage, or their company(e.g., children).

Some clients might stick to preselected routes (e.g., the short-est) and only look for rides along their route. Other costfunctions of clients might be travel time, number of trans-fers, or trip fares. Clients with a desire to optimize thesecost functions will accept detours, as long as they promiseto reach the destination for lower cost. For some clients,shorter travel times are more important than trip fares,while budget clients favor cheaper rides. Fewer transfers aremore attractive to clients who appreciate comfortable trips,while scenic views would be a cost function (to maximize)for tourist clients. Frequently clients balance these factorswith some subjective weighting. Furthermore, clients canhave preferences, such as for types of hosts, or for specificprofiles of vehicle drivers.

Another factor to consider is the environmental knowledgeof the client. While we generally assume that the clienthas knowledge of the street network, it makes a differencewhether the client knows the network and time tables ofmass transport, or typical traffic patterns in the city.

3.2 Mass transport

Mass transport in a city includes buses, trains and trams,subway, and ferries. Generally, mass transport vehicles sup-ply a larger passenger capacity compared to other means oftransport, although with less comfort and privacy. Travelfares are relatively cheap, especially with flat fare structureson longer distances, or with tickets that are interchangeablyvalid on various modes of mass transport. Frequently faresare charged by time regardless how far to travel, but otherpayment systems exist as well.

Mass transport follows fixed timetables, typically with largergaps between midnight and early morning and varying fre-quency over the day. They run on predefined routes backand forth, and passengers are only allowed to get on or offat stops. This means that mass transport does not providedoor-to-door transport, nor does it reach some areas in thecity at all. Some mass transport modes run on their ownline network, e.g., trains, trams and subway, or on reservedlanes, and are less affected by other traffic. This meansthat mass transport vehicles can be faster than street trafficbound vehicles.

3.3 Taxi cabsTaxi cabs are another popular means of transport. Taxis aremore comfortable and convenient compared to mass trans-port. Taxis can reach every location in a city’s street net-work, and can be called at any time of the day. Passengerscan head directly to their destinations without compulsiveintermediate stops or transfers. Detouring, change of desti-nation, and stopovers are also possible during travel.

The main disadvantages of taxis are a limited passengercapacity, and correspondingly, a high trip fare. Normally,taxis have about four seats for passengers, but these are onlyshared for a group having the same trip. Taxis are chargedby a combination of travel distance and time; sometimes aflag fall is added. This means that taxis are more suitablefor individual travellers or small groups travelling together,either for short trips, or when time or convenience is morevalued than money.

3.4 Private carsFunctionally, private cars in their function as hosts of sharedrides are similar to taxis: they share the advantage in com-fort, and the disadvantage in low passenger capacity. Thedifference is that private cars are owned by their drivers,and hence, are considered as private space, or proxemics [8].

Private car drivers, if willing to offer a ride, are unlikelyto serve clients off their route. They can pick up clientsanywhere along their own trip, but may provide only partsof the travel of a client. Private car drivers may have morerigid interests and preferences in selecting clients, such asnon-smoking clients, or clients of a specific gender.

Compared to taxi fares, a ride in a private car could be free,if the incentives for the car drivers may be nonmonetary,such as being allowed to use high-occupancy vehicle lanes.Alternatively, they can charge proportional to the travelleddistance, but to lower rates as taxis because their interest ismostly in sharing costs.

Table 1: Node featuresField Type Description

1 x int x coordinate in a grid2 y int y coordinate in a grid

4. FORMALIZATION IN A MULTI-AGENTSIMULATION

This section presents a specification of a peer-to-peer sharedride simulation, with the types of agents and their behav-ior as discussed above. The simulation is implemented inan object-oriented architecture using Java. Various trans-portation agents are designed inheriting from a base class(transportation) agent.

In our peer-to-peer shared ride system, the agents have knowl-edge of their locations within the street network, negotiatewith their neighbors for shared rides, make decisions ac-cording to their desires and intentions, and travel until thenext negotiation takes place. Therefore, this system can beseen as a geographic automata system: it has states, andstate transitions, in particular finding a ride, depend on theneighbors.

To implement geographic automata systems, Benenson andTorrens [3] suggest to establish a spatially restricted net-work with immobile and mobile agents, neighborhood rela-tionships and behavior rules. Due to their interest on urbanobjects, such as buildings or residential addresses, they use acellular network. In contrast, agents in shared ride systemsmove in street networks, and hence, we use a grid network tomodel a real street network, with nodes representing streetintersections and edges the street segments.

4.1 Environmental parametersAgents travel along edges, but are only at nodes allowedto take or change a ride. In the grid network, nodes havecoordinates (x, y), which represent index numbers of gridcolumns and rows. Since coordinates form already a primarykey, we do not design an additional identifier field for nodes.With identifiers, an extra reference list between identifiersand coordinates would be needed, which results in additionalcomputations. Also, the length of message is of no concern ina simulation, and hence a key of two fields is as appropriateas a key of one field. Nodes are specified in Table 1.

Edges, the connections between neighboring nodes in thisgrid, have a length of unit size. The dimension of the gridnetwork is scalable by setting its width and length in termsof numbers of edges. Additionally, an internal clock is em-ployed to synchronize the behavior of the agents. With re-spect to the experiments in the simulation, several environ-mental parameters are designed to control the communica-tion range and communication mode between agents, includ-ing a counter for the negotiation messages. Furthermore, theagent behavior is relative to their type, and the number ofclients and diverse hosts are specified by parameters. Theclass simWorld is specified in Table 2.

4.2 Communication protocol and strategiesIn a peer-to-peer shared ride system, clients depend on trans-portation information from all hosts to plan optimal trips.

Table 2: Simulation world featuresField Type Description

1 length int length of simulation grid system2 width int width of simulation grid system3 unit int length of a grid edge (=1)4 comRange int communication range (number

of hops of messages)5 clientNum int number of clients6 hostNum int number of hosts7 msgNum int count of the total number of

broadcasted messages8 time int current simulation time (starts

at 0)

Table 3: Message featuresField Type Description

1 type char request r, offer o, booking b2 route [node] requested or offered route3 time int start time of the route in the

message4 agents [int] record of all identifiers of agents

that transfer this message5 speed float speed of the original sender of

this message

However, in the dynamic traffic, an individual client maynot reach or may not want to reach all hosts in the streetnetwork. This means that clients have to plan a trip withlocal knowledge only. Nagel suggests that trip plans alwaysinclude a start time, a start position, a destination and asequence of nodes in between [12]. In shared ride planning,agents are additionally interested in the agents involved inthe trip, and arrival times. To enable negotiations betweenagents for trip plans, a communication protocol is designedfor messages of the structure specified in Table 3. The de-tails of the communication model and protocol are specifiedby Winter and Nittel [18].

In a peer-to-peer system agents radio broadcast messages totheir neighbors. Their radio range is limited according to thebroadcasting technologies, such as Bluetooth and WiFi, andthe broadcasting power. Distant agents can be reached byforwarding messages (multi-hop broadcasting). For a peer-to-peer shared ride system the communication window—thesynchronized time all agents listen and broadcast—requiresto be long enough to accomplish a complete negotiation pro-cess, consisting of a request, offers, and a booking. Thismeans that from the previously investigated three communi-cation strategies—unconstrained, short-range and mid-range[18]—the unconstrained communication strategy is not fea-sible in reality. Unconstrained communication means thatmessages flood to the deepest agents in network, as long asagents are connected (comRange= ∞). The other two arelocal communication strategies. In short-range communica-tion, agents only communicate to agents within their radiorange (single-hop, comRange= 1). In mid-range communi-cation, agents forward messages within several hops (com-Range> 1). The negotiation process will be simulated fordifferent communication ranges to investigate trip planningwith different levels of transportation network knowledge.

Figure 1: The cycle of negotiations and movementswithin one time unit.

Figure 2: Class hierarchy of agents.

4.3 The negotiation mechanismWe need a mechanism to process the negotiations (Figure 1).Clients initiate a negotiation by sending a request. Hosts re-spond with offers, and the negotiation finishes with a book-ing made by the client. These three phases happen sequen-tially. All requests, offers and booking messages are in theformat of message, and are identified by type and the origi-nal sender in agents. After each negotiation the simulationclock increments. Because agent travel changes dynamically,agents do not need to keep previous negotiations in mem-ory. Therefore, there is no cancellation phase integrated,because booked rides are regarded as being cancelled whenno rebooking/confirmation happens in the following negoti-ation, or no client/host show up for an appointment.

So far, only one client is generated in an individual simula-tion (clientNum=1). All hosts serve for this client. In thiscase, hosts do not need to decide which client to contribute,and there is no competition among clients.

4.4 Agent parameters and behaviorAgents are designed in a class hierarchy (Figure 2), becausethey all have some common features and behavior. Thesecommon features and behavior are identified and encapsu-lated in the base class agent.

Common features include the agent’s identifier, its speed,its type, its state, a reference to its current simulation en-vironment, and some information on its travel plan, such ascurrent position and destination, a temporary container ofnegotiation messages. The travel route contains departureand destination, and for some agents the nodes in between.For investigation purposes, a second container stores detailsof booked shared rides. All agent features are listed in Ta-ble 4. Common behavior includes how to move to the nextnode, how to listen to neighbors and how to obtain knowl-edge about current position and state. The agent behavioris specified in Table 5.

Table 4: Agent featuresField Type Description

1 id int unique identifier2 speed float speed of agent, in units of

edges3 type char agent’s type: client c; host h4 time int the time receiving an of-

fer/booking5 state char agent’s current state: mov-

ing m; on a ride t; waitingw; stopped e

6 position int index of current position inroute array

7 route [node] route array, the first is startpoint, the last is destination

8 messages [message] container of received mes-sages at each negotiation cy-cle. This list is updatedwhen a message is received,and it is cleared when a newnegotiation cycle starts

9 services [message] (not accessible for agents).For clients: details of all of-fers so far. For hosts: de-tails of all booking messagesreceived so far

10 world simWorld reference to the current sim-ulation environment

Table 5: Agent behaviorName Input Output Description

1 move null null move to the nextnode in route (ab-stract class)

2 listen message null update current mes-sage container

3 getPos null node get agent’s currentposition

4 setState char null set agent’s state (in-put is a state)

Table 6: Client featuresField Type Description

1 search LPA instance of shortest path searchclass

2 sharedT int start time of the most recentshared ride

3 mobile Bool client can walk (TRUE) or not(FALSE)

Table 7: Client behaviorName Input Output Description

1 request message null broadcast requestmessage

2 book null message book an offer3 addNode int,

nodenull add a new node af-

ter a specified posi-tion in client’s routeto an intermediate orfinal destination

4 getNext null node get the position ofnext node (if at someposition in betweennodes)

5 move null null decide whether andwhere to move

The classes client and host are derived from agent, and haveadditional properties and characteristic behavior. Their states,travel routes and current position can change over time, buttype and speed are constant within a simulation.

4.5 Client agentsIn our simulation, there are two types of clients: immobileclients, taking rides only, and mobile clients that are alsoable to walk. The first type of client needs to be picked upfrom their location. They cannot walk and must wait un-til a vehicle will pick them up for a ride. The second typeof client is able to walk and can travel to a public trans-port line or other location if they can get a ride sooner. Forthem, a (time-dependent) shortest path algorithm is neededfor trip planning. The algorithm applied by these clientsis the heuristic lifelong planning A* algorithm. This algo-rithm is adaptive to a dynamic network. Given various costfunctions (e.g., travel time or trip fare), this algorithm al-lows clients achieving different goals such as the quickest orthe cheapest trip. Client features and their behavior arespecified in Tables 6 and 7.

4.6 Host agentsHosts have limited passenger capacity. Hosts need to decidehow to contribute to requested routes: they can offer toshare sections of their own travel plans that match withrequests, or they can leave their predefined travel route andmake a detour for clients. Alternatively, hosts can leave thedecision in the hands of the clients, by offering their travelroute ahead no matter how relevant this is to the request.Host features are shown in Table 8, and their behavior inTable 9.

Table 8: Host featuresField Type Description

1 capacity int number of seats2 detour Bool willing to make a detour for the

client (TRUE) or not (FALSE)

Table 9: Host behaviorName Input Output Description

1 capacity null null check available seats2 offer Bool message ride offer in two

modes: all routeahead (TRUE); con-tributing edges only(FALSE)

3 match [node] float match request withown travel plan,if matched, returnthe start time ofthis matching travelroute; otherwise,return −1

4 indexOf node,int

index find a node from aspecified position ina route array (neededfor matching)

5. SIMULATING SHARED RIDES WITH DI-VERSE AGENTS

The specified peer-to-peer shared ride system simulation istested for different types of agents. For the purpose of thetest, the optimization criterion of travel time was chosen,looking for the quickest trip. The simulation produces out-put in the form of text, which can be stored or visualized.Here the stored tables of 1000 simulation runs each are sum-marized in diagrams.

Particularly, we have classified hosts into three groups: pri-vate cars, taxis, and mass transport. Table 10 shows thecomparison in terms of values of features.

5.1 Types of clientsFigure 3a shows the average time of shared rides by variousclients, and Figure 3b shows the corresponding numbers ofbroadcasted messages. The types of clients compared are:an immobile client who sticks to the geodesic route, a mobileclient who sticks to the geodesic route, and a mobile clientwho is willing to make detours. Each client departs at (0, 5)and heads to the destination at (10, 5), which is a trip of ten

Table 10: Parameter settings of host typesType Capa-

citySpeed Route Detour Others

1 privatecar

2 1 fix FALSE

2 taxi 1 1 variable TRUE3 mass

trans-port

10 2 pre-defined

FALSE schedule

Figure 3: Trip times of immobile clients, mobileclients that stick to the geodesic route, and mobileclients making detours (a), and the correspondingnumbers of messages (b).

edges along the geodesic route. Clients consistently choosea cost function of travel time. Mobile clients have a walkingspeed of vc = 0.25 edges per time unit—they need four timeunits to finish travelling along an edge—, compared to thehost speed of vh = 1.

In this experiment, hosts are homogenous: all are privatecars. The numbers of hosts vary from 24 to 144. Each hosttravels over twelve time units along a travel route that isgenerated randomly. The simulation was run on a world of11× 11 nodes for a communication range of three.

In our experiment, the two types of mobility do not makemuch difference. This is partly the case because of the choiceof hosts. For example, mass transport routes make moredetours, and reward this effort. It is also correlated with therandom routing of cars—they rarely provide longer rides—, and with the communication range: to pick up a ride inthe next parallel street, a client has first to walk over oneedge or four time units. What comes out clearly, though, isthe advantage of being mobile. In terms of communicationcosts, clients willing to make detours get more offers. Thisincrease in broadcasted messages remains in limits.

5.2 Shared rides by carsPrivate cars are specified by a speed of one edge per timeunit, a route that is determined by random at their depar-ture and from which they will not deviate, and a low pas-senger capacity (Table 10). The latter is not relevant in asimulation with one client only. The simulation that tests apeer-to-peer shared ride system consisting of one immobileclient. This type and specification of hosts has been investi-gated in previous work [18]. In that work it was shown thatmid-range communication delivered trips nearly as quick aswith unconstrained communication, for all densities of hosts.For details we refer to that work.

However, with the introduction of different client types (Fig-ure 3), it turns out that mobile clients, due to their increasedchoices, have advantages over immobile clients. If traveltime is the optimization criterion, a shared ride system forprivate cars would lead to combined trips of rides and walks.

5.3 Shared rides by taxisTaxis are specified by a speed of one edge per time unit,a route that is determined by random at their departure,but from which they are willing to deviate any time (if notoccupied), and a low passenger capacity (Table 10). Withthis specification, a peer-to-peer shared ride system for taxisleads to client trip times close to the theoretical optimum,which is defined by the distance of the client’s departure anddestination, and the host speed. A taxi comes as soon aspossible and heads directly to the client’s destination with-out detours. Only the density of taxis determines the (av-erage) waiting time of the client. The higher the density,the shorter becomes the waiting time. Since the simulationresult is predictable, we abstain from a diagram.

Communication range has a minor impact in a a peer-to-peer shared ride system for taxis. Since always the nearest(free) taxi will be chosen, and no other agents are present inthis system, the nearest taxi has either to be in direct com-munication range, or currently occupied taxis can bridge bymessage forwarding. Only if taxis are employed in a sharedride system together with other hosts of larger numbers, thecommunication connectivity established by the other hostswill make a significant difference.

5.4 Shared rides by mass transportMeans of mass transport are specified by a speed of twoedges per time unit (twice the speed of other hosts), by pre-defined and fix routes, and by scheduled frequencies. In thisexperiment the frequency of a bus is set to every ten timeunits, and the world is of size 21 × 21. We distinguish twocases (that reappear in the mixed simulation of Section 5.5).Case 1 is a simulation with a bus line through the parallelstreet of the client’s geodesic route. Case 2 is a simulationwith a bus line overlapping with a major part of the geodesicroute. Both cases are investigated for (a) buses being theonly hosts in the simulation, (b) buses being hosts among480 occupied private cars, such that private cars establishcommunication connectivity but do not offer rides, and (c)buses and bus stops being the only hosts in the simulation,such that missing communication connectivity to the busesis balanced by the presence of the bus stops within the directcommunication range of the client.

Figure 5: Two bus lines, a client’s departure in thecenter and destination at the far right end.

Figure 4 demonstrates the results, both in terms of aver-age trip times as well as numbers of broadcasted messages.It turns out that the presence of bus stops is of advantagecompared to both other scenarios. The numbers of mes-sages are within limits larger than for buses only, but theaverage travel times are significantly shorter. Bus stops alsohelp to reduce the communication effort in the presence ofother hosts that are willing to establish connectivity withincomRange.

5.5 Shared rides deploying all types of agentsIn the final experiment we deploy all types of hosts for amobile client willing to make detours. Results in this casedepend completely on factors such as host densities andcomposition of the hosts. However, this experiment candemonstrate two properties of the system: that (i) mid-range communication is consistently nearly as effective asunconstrained communication, and that (ii) mid-range com-munication is consistently more efficient. The latter prop-erty increases radically with increasing host density, i.e.,with increasing connectivity in the agents’ network. Otherproperties can be studied as well, for example, finding abalance in the composition of hosts.

Figure 5 shows a world of 21× 21 nodes, two bus lines, anda client’s geodesic route from the center to the right. Notethat this client is willing to make detours, hence, it doesnot have to follow the geodesic route. Within this worldthe shared ride system consists of a constant number of 120agents in three different compositions (Figure 6): (a) 24taxis, 24 buses (with a frequency of two time units), and72 private cars, (b) 24 taxis, 24 buses, 24 bus stops and 48private cars, and (c) 48 taxis, 24 buses and 48 private cars.

For these three compositions we observe a decreasing traveltime from (a) to (c). The effect of bus stops, documentedbefore already, can be recognized again (b), but a largernumber of taxis exceeds their effect (c). The decrease ismoderate, though, which means that (on average) rides in-volving taxis and buses are taken. These rides reduce triptimes significantly compared to the ‘cars only’ scenario for

Figure 6: A world of 120 host agents of differentcomposition, and a mobile client.

Figure 4: Means of mass transport, although they are bound by their routing and schedules, have an influenceon trip times. The two investigated cases are explained in the text.

Figure 7: Different compositions of 120 hosts.

mobile clients in Figure 3. This result is generally con-firmed by Figure 7, which compares different compositionsof 120 hosts for mid-range communication only. Note thatthe worst trip time is 40 time units (the client walks), andthe best trip time in the present scenario is 7.5 time units(taking the bus, and finding other rides for the rest withoutdelay).

At the same time, the number of broadcasted messages in-creases from (a) to (b). This is triggered by the large num-ber of offers made by the bus stops. Bus stops currentlydo not filter their responses by relevance, but offer eachapproaching bus. This means that this figure for (b) canbe reduced by future modifications of the bus stops. Forthe two compositions in (a) and (c), the broadcasted mes-

sages in each negotiation should be the same, but since (c)leads to shorter travel times, or less negotiations, the totalsum of messages is smaller in (c) than in (a). The cur-rent host density (120 host on 441 nodes) is relatively low,which means that communication connectivity is low. Fur-ther experiments with higher densities of hosts shows thatthe numbers of broadcasted messages are more rapidly in-creasing for unconstrained communication than for the localcommunication strategies.

The interesting result, though, is a comparison with the im-mobile client in the ‘cars only’ scenario, at the same hostdensity (Figure 3a, upper curve). The results for the immo-bile client are repeated here from [18]. Compared to theirscenario, we can observe that with the introduction of othertypes of agents (i) the (average) trip times change signif-icantly, effectively being reduced by more than 50%, and(b) mid-range communication is still nearly as efficient thanunconstrained communication, with the number of broad-casted messages by mid-range communication being roughlythe same.

5.6 Multi-criteria optimizationThe previous investigations assume clients pursuing shortesttravel time trip planning. In practice, other criteria arealso used, such as trip fare and numbers of transfers. Inthis case, multiple criteria are employed and optimized byclients. That allows clients, for example, to look at a relativequick and cheap trip. Imagine that travel time, trip fare andnumbers of transfers are chosen as three criteria, and linearprogramming is used to balance the importance of them.Table 11 demonstrates the multi-criteria optimization.

In this case, trip 2 is the optimal one: it is the trip of shortesttravel time, of no transfer, but is the most expensive one.With other weighting of the criteria other trips could be

Table 11: Multiple criteria optimizationTrip Time Fare Transfers Total costslinear weights: 0.33 0.33 0.33

P1 a → b, b → c 4 4 1 3.02 a → c 3 5 0 2.73 a → d, d → e,

e → c 6 3 2 3.7

preferred. Assume, for example, that a client has a strongerpreference for a cheaper trip. This client might choose arelative weighting of (0.2, 0.6, 0.2), and find the trips 1 and3 equally optimal.

6. DISCUSSION OF THE RESULTSIn the experiments, the characteristic parameter is the den-sity of agents, not their absolute number. Various sizes ofgrid networks with the same agent density will influence thenumber of messages for the unlimited strategy only—theone that is not realistic and used only for control purposes.Larger sizes of grid networks allow longer trips, but even thisis not a critical change of the concept. Hence we expect thatour results hold for longer trips, and also for other forms ofstreet networks.

Walking clients have the risk of missing potential rides dur-ing walk. For example, a walking client can see a bus pass-ing along if this bus did not exist at departure time of theclient, or if the bus was still out of the client’s communi-cation range. This risk can be reduced by choosing com-munication ranges large enough to provide the client withall relevant offers for this period. But extra communicationcosts energy.

Another arbitrary design of the simulation is that buses arerunning on parts of the geodesic route. As discussed before,the restrictions of buses, such as pick-up at stops only andfixed timetables, limit their occupation.

Figure 4 shows that under some conditions buses, if travel-ling along parallel streets, can even not contribute to clienttrips at all. This happens with the communication rangebeing not large enough to inform the client in time to startwalking to the parallel street. Globally adapting the com-munication range to the speed of the hosts helps in thissituation (if other hosts establish multi-hop connectivity).This way, the communication effort is increased significantly,which contradicts our intentions and hypothesis. Alterna-tively, the agency of bus stops helps. In our simulation, busstops currently offer each approaching bus, in any distance,in an individual offer message. This increases the numberof messages in Figure 4, but can be reduced by offering onlyrelevant buses.

So far, all trips are designed to achieve locally the quickesttrip. In practice, clients probably look at other criteria aswell, such as fares and transfers. The employment of othercriteria is expected to change the results. The employedlifelong planning A* algorithm is adaptive to any criteria,such as fares or transfers. However, only one optimal pathis returned by this algorithm each time, and alternatives—for example for ranking with multiple criteria—are not pro-

vided. To achieve multi-criteria optimization, all possibletrips need to be calculated under these criteria separately.Then linear programming can be used to balance factors andfind an optimum.

7. CONCLUSIONS AND OUTLOOKPrevious research studies the behavior of a peer-to-peer sharedride system with an immobile client following a geodesicroute, and private cars. It is found that for a peer-to-peershared ride system mid-range communication is both effi-cient and effective [18]. In this paper, we extend the pre-vious research with mobile clients, various types of hosts,and other agents, applying mid-range communication in ourexperiments. We went out to show that employing othertypes of agents change the trips significantly, but mid-rangecommunication is still the preferable range.

Reviewing the results from our simulation, we can see thatmultiple types of agents enrich the choices of clients and asa result lead to trips of generally lower costs. The largestimpact has a peer-to-peer shared ride system with mobileclients and all types of host agents, since it provides thelargest choice for a constant communication range. Mid-range communication still delivers trips of durations closeto a (fictional) unconstrained communication range, but hasmuch lower communication costs. Hence, the hypothesis hasbeen proven.

Besides all the improvements in reducing trip times, oneproblem remains: trips derived from local knowledge (of anycommunication range) may not be optimal from a globalview. Better rides provided by distant hosts and hosts en-tering the traffic after the client has made a booking arealways possible, and can be documented from a subsequentanalysis of the simulation protocol. This problem can beapproached by more intelligent wayfinding heuristics of theclients. Clients could, for example, learn from experienceand use this knowledge in predicting chances of being pickedup at specific nodes. For this purpose, a client could, for ex-ample, exploit a hierarchy in the street network, or knowntraffic counts at particular intersections, to assess potentialtransfer points in the trip planning process. This idea isinvestigated elsewhere [6].

Related to more intelligent wayfinding behavior is the re-quest for multi-criteria optimization. For example, clientsmay be interested to reduce their number of transfers andtheir trip time. The introduction of different fare models,and the choice of the cheapest trip (or of a balanced cheaptrip in a multi-criteria optimization), will further allow totest economic concepts of a peer-to-peer shared ride system.This requires a change in the mobility model of the agents inthe simulation. Although a random walker mobility modelis sufficient for our interests in this paper (except for masstransport vehicles), it is no longer sufficient when numbers oftransfers in a realistic traffic scenario shall be investigated.For private cars for example, regular traffic patterns can beintroduced, such as back and forth between home and work,as it is done in traffic micro-simulation [15].

Another future extension of this system comes with admit-ting other clients in the simulation (clientNum> 1). Thenthe passenger capacity of the hosts becomes a critical re-

source. Clients would compete with each other, which mightrecommend more booking ahead. But more aggressive book-ing strategies conflict with the hosts’ interests of travellingwith occupied vehicles, since travel plans are highly dy-namic. Balancing these interests need to be investigated.

Some steps to exploit properties of dynamic transport net-works for trip planning from local knowledge are made, butmany other questions lie ahead.

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