Implementation of vehicle relocation for carsharing services in the multi-agent transport simulation MATSim

Transcription

1 Implementation of vehicle relocation for carsharing services in the multi-agent transport simulation MATSim Date of submission: Stefan Paschke ETH Zürich, 0, Zürich phone: fax: +-- stefan.paschke@gmail.com Milos Balac IVT, ETH Zürich, 0 Zürich phone: fax: +-- milos.balac@ivt.baug.ethz.ch Francesco Ciari IVT, ETH Zürich, 0 Zürich phone: +-- fax: +-- ciari@ivt.baug.ethz.ch Words: 0 words + figures = word equivalents

2 Paschke, S., Balac, M. and Ciari, F. ABSTRACT Operators of free-floating carsharing systems typically encounter the problem of imbalances in their vehicle fleets, caused by demand fluctuations over the course of one day, or from one day to the following. In order to provide a high level of accessibility to their service with a minimum number of vehicles, they must consider relocating unused vehicles. While some strategies to accomplish this have been successfully implemented, both in simulation and in practice, the set of tools required to represent carsharing in agent-based simulations is still incomplete. Agent-based simulations are particularly well suited to this task, because they allow modeling the interaction of supply and demand at individual level. This paper describes the implementation of the relocation agents within MATSim framework as an extension to the previous work on carsharing. The implementation was tested using a simple operator-based relocation strategy.

3 Paschke, S., Balac, M. and Ciari, F INTRODUCTION The presence and acceptance of free-floating carsharing worldwide has grown rapidly over the past few years. After an experimental setup in Singapore and the first commercial system implemented in Ulm in 00 (), free-floating carsharing has become available in many European and North-American cities, often surpassing other types of carsharing, because of the increased flexibility, in terms of membership and number of vehicles right from the start. Traditional implementations of carsharing, referred to as station-based, typically require their users to pick up vehicles at designated parking lots (stations). In most of the cases, vehicles have to be returned to the same station. The rental rates, and their structure, encourage short trips, and discourage all-day or overnight rentals, which make them suitable for non-customary trips, such as fetching heavy or bulky items, but typically prevent their use as part of one s daily journey, i.e. for commuting. Free-floating carsharing, on the other hand, uses public parking space. It allows users to pick up any nearby vehicle, drive to their desired destination and park on any available parking lot within an area that typically includes the center of a city and some of its inner suburbs. While the general intention behind the implementation of such a system is to provide a form of mobility that complements the transportation mode used for daily recurring trips (usually public transport) this might make it suitable for regular, perhaps even daily trips. The quality of service of such a system depends on the availability of vehicles in the immediate vicinity of the user. As the overall demand for the service can be assumed to show temporal and spatial patterns, imbalances in the distribution of the vehicles are likely to occur - i.e. many users trying to get from a residential to a business area in the morning will result in a shortage of vehicles in the first area and an oversupply in the latter. Of course, such predictable demand patterns could be met by increasing the number of vehicles and placing them where they are needed in advance, on a daily basis. This would, however, probably decrease the number of rentals per vehicle, while it would increase the number of occupied parking lots. And since free-floating carsharing systems typically operate in densely populated urban areas, where parking space is scarce (and policy makers willingness to use public ground for parking generally decreases), ways to satisfy this demand with the lowest possible number of vehicles must be found. Therefore, strategies for dynamical redistribution of the fleet should be considered. Vehicle redistribution indeed, has been widely researched in the last years and several operators implemented a redistribution strategy. Redistribution approaches can be divided into user-based and operator-based approaches. User-based strategies typically consist of incentives intended to make the user change his or her plans in regards to picking up or dropping a vehicle where it has a higher chance of fitting into general demand, i.e. motivate the user to walk a longer distance to pick up a vehicle that would otherwise not be used, or to leave it in a busy street rather than in an area with less activity at that time of the day (). Operator-based approaches require a system dispatcher who actively relocates vehicles by appropriate technical means. In order to estimate the characteristics, particularly the possible usage induced by an increased number of vehicles, of both station-based and free-floating carsharing, both types have been implemented in agent-based transport simulation software. This approach is expected to be an advancement compared to previous solutions proposed as it captures the demand which arises because of the redistribution, whereas most of the existing literature on the topic assumes static demand. Such an approach implies a high computational burden, but makes the ex-ante testing of different redistribution strategies possible. This means that operators, on top of simulations

4 Paschke, S., Balac, M. and Ciari, F supporting operations on a daily basis, have an additional tool, which can be used for the strategic planning of a carsharing system. BACKGROUND Introduction of one-way station based (from now we will refer to this kind of service as one-way) and free-floating carsharing has brought more flexibility for the users, but also new problems for the operators. Since the vehicles in these two services can be returned and parked at any station for one-way service or at any public parking spot within the service area for free-floating, imbalances in the vehicle distribution can occur. Therefore, it is important to study these imbalances and to find solutions in order to reduce these negative effects. In the past, researchers have already dealt with this problem (( ) among others) and an extensive literature review on different relocation strategies was conducted by (). On the other hand, simulation frameworks that are able to test the impact of relocation strategies are very few (,, ). However, the biggest disadvantage of these frameworks is the fact that they are not able to deal with the changes of the demand triggered by the change of the supply. Nevertheless, this kind of reaction is important in order to capture the full impacts of a relocation strategy, since changing the vehicle locations will trigger a change in the demand. The ability to take this aspect into account is particularly important in the strategic planning of a program (i.e. before the launch or before a substantial enhancement of an existing program). As mentioned previously, relocations can be operator-based or user-based, and in this work we will focus on the latter. Operator-based relocation strategies Once the distribution of vehicles in a (one-way or free-floating) carsharing system becomes imbalanced, the system operator intervenes by dispatching employees to relocate vehicles. This can be done in a number of ways: Teams of two workers can use a service car to drive to the vehicle to be relocated. One of them will drive it to the destination, while the other follows to pick him up in the end. If the relocations are to be executed during public transport service hours, the driver can use public transport to get to and from the vehicle. Another way can be the use of folding bicycles that can be carried along in the trunk. In some cases, car transporters are used, and in future scenarios, autonomous vehicles might become an option. Relocations can also be combined with refueling, or if electric vehicles are used, recharging the vehicles, as well as other types of servicing. The level of fuel or battery power can be taken into account when deciding which vehicle to relocate. Relocations can be carried out once daily (typically overnight), several times per day, or continuously. In any case, spatial and temporal demand patterns must be identified. This is generally done by dividing the service area into smaller units referred to as blocks () or zones (). The size of these zones is determined by the distance a user might find acceptable to walk, which is assumed to be about 00m. While the user is not aware of these zones, their size determines if a relocation action will be successful. If they are too big, a vehicle, that will typically be placed anywhere within the target zone, might still not be accessible to a particular user. For each zone, the number of available vehicles (supply) and the number of requests that are expected for a particular interval (demand) can be determined, the latter by using historical data, in a comparable interval. Relocation strategies will define rules for moving vehicles between zones, to better match demand. Weikl and Bogenberger () describe a very

5 Paschke, S., Balac, M. and Ciari, F sophisticated, field-tested system that uses two levels of relocation zones to redistribute vehicles at the end of an operating day. Barrios and Godier () compare a similar, yet simpler periodic approach to an operator continuously redistributing vehicles during the day. Both find that the increase in revenues from the additional rentals can compensate for the substantial costs of the relocation actions. METHODOLOGY The work presented in this paper makes use of a large-scale agent-based transport simulations, called MATSim. The software, through the agent paradigm (), simulates one day in the life of individuals. Each agent in MATSim has a daily plan of trips and activities, such as going to work, school or shopping. The initial demand, consisting of one plan per agent, is typically generated based on census data and mobility surveys. Depending on the simulation goal, street networks, facility locations and public transport routes and schedules are added with an appropriate level of detail. The plans of all agents are then executed, resulting in traffic flow along network links, which can, again depending on the goal of the simulation, be restricted by network capacity, availability of parking space or restrictions in public transport systems. At the end of the execution, each plan is scored and assigned a utility. In general, agents aim at performing activities (e.g. working, shopping), which increase the utility, while the time spent traveling between activities decreases it. Agents try to maximize the utility of their plans in a process called re-planning. Agents are free to change routes, departure time, travel modes, activity location or use other innovative strategies. The modified plans are then executed and scored again, the sequence of execution, scoring and re-planning is called an iteration. After a certain number of iterations, the plans with the lower scores are deleted, while the plan with the highest score is selected (to be modified again in the following iteration). Because all agents simultaneously modify their plans, their decisions are mutually dependent, e.g. one agents decision to start earlier on his journey will benefit the other agents traveling on the same route at the original time. The simulation is an iterative process, which is stopped when a point of equilibrium, also called relaxed demand, is reached. This state corresponds to a user, or Nash, equilibrium. More details about the conceptual framework and the optimization process of the MATSim toolkit can be found in Horni et al. (). Carsharing in MATSim The introduction of carsharing as an alternative mode of transportation in MATSim was discussed by Ciari et al. () in 00 and subsequently implemented for Case Studies in Berlin and Zurich ( ). While the first implementation by Ciari et al. () only covered station-based carsharing and carsharing vehicles were not part of the actual traffic flow (a common practice for transport modes that can be expected to require only a modest share of road capacity), the later work did not have these limitations. In more recent work (, 0), the effect of parking limitations on different carsharing types was also added to the software. Currently, both station-based and free-floating carsharing are available as a mode of transportation to MATSim agents. Carsharing is a membership program, so access to the service for individual agents can be defined in the agent attributes, along with sociodemographic parameters. After finishing his activity, if the next part of the agent s agenda is a free-floating carsharing trip, the agent follows the steps below in a given order:

6 Paschke, S., Balac, M. and Ciari, F Find the closest available vehicle and reserve it (make it unavailable for other agents). Walk to the vehicle. Drive to his trip destination (in the case of station-based systems, to the station closest to the destination). Park the vehicle, and thereby make it available again. Walk to the next activity Fees consisting of a time dependent as well as distance dependent component allow realworld price plans to be taken into account in the agents utility function. A comprehensive overview of the current state of the art is presented in Ciari et al. (). Relocation Agents The most important part of this work is the implementation of the relocation agents in MATSim. These agents are not part of the general population, and their behavior is only simulated when they need to relocate a carsharing vehicle. The agents behavior is as follows: The dispatching service, based on the relocation algorithm, asigns each agent which vehicle it needs to relocate. The agent can use any transportation mode in order to reach the vehicle to be relocated (in this work, we have chosen it to be a bicycle, in case of a car it would interact with other vehicles on the network while driving to the vehicle to be relocated) Upon reaching the vehicle, the agent drives it, while interacting with other vehicles on the network, to the assigned destination (based on the dispatcher algorithm) Upon parking the vehicle the agent goes back to the dispatch center and waits for the next assignment. Relocation agents do not posses a daily plan like a regular agent in MATSim, but are completely controlled by dispatch center which assigns each relocation agent a job at certain point of time, which is then executed during the simulation. Relocation agents are completely independent from the relocation strategy used. Therefore any relocation strategy can be used, which would allow us to investigate many different approaches. The number of relocation agents can be predefined and their initial locations can be easily manipulated. This is important especially when different optimization goals are pursued (profit maximization, user satisfaction maximization, rentals maximization, etc.). In order to test this implementation, we used a simple relocation strategy described below. Estimating free-floating carsharing demand The distribution of free-floating carsharing requests over the course of one day reflects the different purposes of the rentals. While some vehicles may be used to commute to work in the morning and back in the evening, trips between two activities during the day (e.g. between two work locations) and return trips after leisure activities in the evening can be observed, to just name a few. Some of the resulting imbalances could be tackled by relocating vehicles once daily, typically overnight. This would mean to "reset" the vehicle fleet, by returning it to an ideal starting position. If the relocations are to be performed several times a day, or even continuously, the resulting gain in vehicle accessibility, and thereby, the number of rentals, should be even higher. Real-world relocation strategies typically use historic data from previous days with similar

7 Paschke, S., Balac, M. and Ciari, F characteristics to estimate the demand. Since MATSim simulations usually cover a single day, such information is obviously not available within one run of the simulation. While data from previous runs could be used, this would require a great number of simulations, while the demand pattern would still remain static. Instead, the work presented in this paper is based on the assumption that data from previous iterations can be used as estimates. This allows for a highly dynamic model of supply and demand, where the agents can not only adapt their behavior to the supply, but the supply can also be adapted to the agents. The service area in which free-floating carsharing is available therefore is partitioned into non-overlapping polygons, referred to as relocation zones above. They are drawn in a "sensible" way, i.e. they should not contain obstacles that would prevent access to a vehicle location in the real world, such as rivers, railway yards or freeways. Near the city center, vehicle density is higher and the dimensions of the polygons are about equivalent to the maximum walking distance an agent might find acceptable (00m according to Barrios and Godier ()) and Weikl and Bogenberger (). In the suburbs, where fewer vehicles are available, they might be larger. Before the start of each iteration, the free-floating carsharing requests from the previous iteration are analyzed. Request data contains the time and GPS position of the moment the booking was made as well as the start and end of the actual booking. Requests are assigned to a relocation zone based on the booking location (not the location of the reserved vehicle). In the same step, the expected number of vehicles to be returned to the particular relocation zone are estimated in the same way. The simulation day is then split into six three-hour time slices, starting at 0:00 in the morning. For each interval and relocation zone, the sum of vehicles available at the beginning plus the number of expected returns is compared to the number of expected requests. Relocation strategies Once the number of vehicles required to satisfy the expected requests for each relocation zone is determined for a time interval, relocations can be dispatched. The relocation strategy used in this work is the following: Relocation zones are ordered by the number of required (or surplus) vehicles, the zones with the greatest number of required vehicles are placed on top of the list. Vehicles are taken from zones at the bottom of the list, while available. Individual aspects of the vehicles, such as the time a particular vehicle has been idle, are not taken into account. On the demand side, a threshold of one vehicle is applied, i.e. relocations are only being dispatched to zones which require more than one vehicle. This is to avoid relocations to outlying zones with random requests. When removing vehicles from a zone where rental requests are also expected (but are outnumbered by the available vehicles or expected returns), a safety factor of % (in other words - increasing the expected number of requests by %) is applied to the expected number of requests to avoid removing vehicles that will be needed. Once a relocation is identified, a relocation agent is dispatched. In the simulation, the relocation agents are implemented as actual agents with daily plans that just consist of standing by at a central location. Once a relocation is dispatched, it changes its plan to travel to the vehicle (using a bicycle), drive it to the destination zone and return to his original location immediately thereafter. The vehicle is delivered to a historic request location inside the destination zone, selected randomly. One relocation agent executes only one relocation per time interval.

8 SIMULATION RESULTS Scenario description The base of the scenario used for this work is a collection of traffic diaries covering approximately. million people living in an area of 00 km around Zurich, Switzerland. The scenario uses the actual road network, with capacities adjusted to the relative sample size. Transport modes available to agents include driving, cycling, public transport, walking and free-floating carsharing (ff cs). To increase ff cs ridership, all agents that possess a driving license were given access to the ff cs system. FIGURE Free-floating carsharing service area. The ff cs service is restricted to an area of approximately km around the center of Zurich. It includes the city center and some of the more densely populated suburbs (Figure ). A total of vehicles are available, initially distributed throughout the service area. The initial density of vehicles is higher near the city center, while in the suburbs, less vehicles are available. This relatively low number of vehicles was chosen in order to create a scenario where the vehicle supply is not larger than the overall demand, and no vehicles would remain idle over the entire day. The execution of the scenario described above, with free-floating carsharing vehicles

9 available in an area of km around Zurich, finds a user equilibrium where approximately 0 to 00 rentals are performed over the course of one day. The relocation strategy used executes up to 0 relocations of vehicles from zones where supply exceeds demand, to zones with a lack of vehicles. Relative to the execution of an identical scenario without relocations, a % gain, can be observed. Spatial-temporal distribution of carsharing demand In Figure, the free-floating carsharing requests, expected for the final iteration and distributed among the relocation zones, is displayed for two intervals, 0:00 to 0:00 and :00 - :00. Only the relocation zones where requests are expected are drawn. The color of the relocation zones corresponds to the number of requests, a light color indicating few, a darker color many requests. While the morning interval shows many requests from residential areas, considerable activity in the city center is already apparent. In the evening interval, activity in the city center increases, but requests in the residential suburbs remain. FIGURE Distribution of ff cs requests 0:00 to 0:00 (left) and :00 to :00 (right). Figure displays the cars available for the same time intervals as Figure. The available cars also include the vehicels that we expect to be returned to this zone in the next hours (the length of the interval). Relocation zones displayed in green have a surplus of vehicles, while red zones need additional vehicles. Again, a darker color indicates higher supply or demand. A closer examination of the relocations shows that, due to the high availability of relocation agents, most relocations can be executed within the first 0 minutes of each interval and can therefore be expected to effectively increase the vehicle availability in the target area. Examination of rental data confirms that the relocated vehicles are being used in the majority of cases. While the relocation strategy implemented in this work generally returned positive results, its outcome could quickly turn negative (i.e. if the demand threshold was omitted or the safety factor was changed). The increased number of relocations (and temporary decrease of vehicles available for rental) would then outweigh the increase in rentals at the zones of high demand that the relocations made possible.

10 FIGURE Availability of ff cs vehicles 0:00 to 0:00 (left) and :00 to :00 (right). DISCUSSION The work presented here extends the already existing implementation of carsharing in MATSim by allowing vehicle relocations by the operator. The relocation agents can be instructed to relocate vehicles in any order, at any time or location, and they can chain relocations, therefore the implementation is very flexible. This allows an investigation of any kind of the relocation strategy. In this work we used a very simple strategy in order to show that the agents are actually able to relocate vehicles in a given way and order. Future work would include testing different relocation strategies with different maximization goals in order to observe their impacts. Here, we used previous iteration as a source of historical data on which we base the relocations in the following iteration. However, in future work we will test different approaches in order to find the one that is able to provide the best relocation outcome. One of the approaches could be to simulate each iteration two times (while only the first one will include the re-planning and the second one will use exactly the same plans as the first one). By repeating the same iteration two times without the change of the demand, it will give us an opportunity to work with more stable data, because every iteration during the iterative process, adds new carsharing demand during the re-planning phase which might influence the outcome of the relocations. CONCLUSION Free-floating carsharing has been introduced only a few years ago, and has since been a remarkable success, its steady rate of growth suggesting that it is still far from reaching its full potential. While it is expected that it will emerge from its niche position and become an important mode of urban transportation in the near future, strategies to provide a high level of accessibility to the system with the lowest possible number of vehicles will become even more important. This is not only because of vehicle imbalances that occur due to the nature of ff-cs demand, but also due to the limitation of parking space in urban areas where these systems typically operate. Operator-based vehicle relocations are one of these strategies. The work presented in this

11 Paschke, S., Balac, M. and Ciari, F paper extends the carsharing implementation in order to allow for operator-based relocation strategies. The framework allows to represent the interaction of supply and demand on an agent level, by allowing agents to optimize several parameters of their daily journeys in an iterative process. Regarding data from previous iterations as historic data, and using it as estimates, this work introduces the possibility to examine this interaction from the inverse side: by dynamically adapting supply to expected demand. This allows for highly dynamic models of free-floating carsharing systems, including operator based strategies. Methodologically, this work adds a missing element to previous work on the topic. Implemented in an open source software framework, it can provide researchers or carsharing operators wishing to implement more sophisticated, operator-based or even user-based relocation strategies a testing ground for strategic planning of a carsharing systems. REFERENCES. Firnkorn, J. and M. Müller (0) What will be the environmental effects of new freefloating car-sharing systems? the case of cargo in ulm, Ecological Economics, 0 ().. Herrmann, S., F. Schulte and S. Voß (0) Increasing Acceptance of Free-Floating Car Sharing Systems Using Smart Relocation Strategies: A Survey Based Study of cargo Hamburg,, Springer International Publishing, Cham.. Barth, M., M. Todd and L. Xue (00) User-based vehicle relocation techniques for multiplestation shared-use vehicle systems, paper presented at the 0th Annual Meeting of the Transportation Research Board, Washington, D.C., January 00.. Kek, A. G., R. L. Cheu, Q. Meng and C. H. Fung (00) A decision support system for vehicle relocation operations in carsharing systems, Transportation Research Part E, ().. Febbraro, A., N. Sacco and M. Saeednia (0) One-way carsharing: solving the relocation problem, Transportation Research Record: Journal of the Transportation Research Board, ().. Repoux, M., B. Boyaci and N. Geroliminis (0) Simulation and optimization of one-way car-sharing systems with variant relocation policies, paper presented at the th Annual Meeting of the Transportation Research Board, Washington, D.C., January 0.. Weikl, S. and K. Bogenberger (0) A practice-ready relocation model for free-floating carsharing systems with electric vehicles mesoscopic approach and field trial results, Transportation Research Part C: Emerging Technologies,, 0.. Weikl, S. and K. Bogenberger (0) Relocation strategies and algorithms for free-floating car sharing systems, Intelligent Transportation Systems Magazine, () 0.. Jorge, D., G. H. Correia and C. Barnhart (0) Comparing optimal relocation operations with simulated relocation policies in one-way carsharing systems, IEEE Transactions on Intelligent Transportation Systems, ().. Weikl, S., K. Bogenberger and N. Geroliminis (0) Simulation framework for proactive relocation strategies in free-floating carsharing systems, paper presented at the th Annual Meeting of the Transportation Research Board, Washington, D.C., January 0.

12 Paschke, S., Balac, M. and Ciari, F. 0. Barrios, J. and J. Godier (0) Fleet sizing for flexible carsharing systems, Transportation Research Record: Journal of the Transportation Research Board,,.. Weikl, S. and K. Bogenberger (0) Integrated relocation model for free-floating carsharing systems, Transportation Research Record: Journal of the Transportation Research Board,,.. Kelemen, J. (00) The agent paradigm, Computing and Informatics, () 0.. Horni, A., K. Nagel and K. W. Axhausen (eds.) (0) The Multi-Agent Transport Simulation MATSim, Ubiquity, London.. Ciari, F., M. Balmer and K. W. Axhausen (00) Concepts for large-scale carsharing system: Modeling and evaluation with agent-based approach, paper presented at the th Annual Meeting of the Transportation Research Board, Washington, D.C., January 00.. Ciari, F., B. Bock and M. Balmer (0) Modeling station-based and free-floating carsharing demand: test case study for Berlin, Transportation Research Record,,.. Balac, M., F. Ciari and K. W. Axhausen (0) Carsharing demand estimation: Case study of Zurich area, Transportation Research Record,,.. Balac, M., F. Ciari and K. W. Axhausen (0) Evaluating the influence of parking space on the quality of service and the demand for one-way carsharing: a Zurich area case study, paper presented at the th Annual Meeting of the Transportation Research Board, Washington, D.C., January 0.. Ciari, F., C. Dobler and K. W. Axhausen (0) Modeling one-way shared vehicle symstems: An agent-based approach, paper presented at the th International Conference on Travel Behaviour Research (IATBR), Toronto, July Balac, M., F. Ciari and R. A. Waraich (0) Modeling the impact of parking price policy on free-floating carsharing: Case study for Zurich, Switzerland, Transportation Research Procedia.. Ciari, F., M. Balac and K. W. Axhausen (0) Modeling carsharing with the agent-based simulation MATSim: state of the art, applications and future developments, paper presented at the th Annual Meeting of the Transportation Research Board, Washington, D.C., January 0.