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Design and Simulation of a Public-Transportation-Complimentary Autonomous Commuter Shuttle
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ScienceDirect Transportation Research Procedia 41 (2019) 240–250 Transportation Transportation Research Research Procedia Procedia 00 00 (2018) (2018) 000–000 000–000
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mobil.TUM 2018 ”Urban Mobility - Shaping the Future Together” - International Scientific Conference on Mobility and Transport
Design and Simulation of a Public-Transportation-Complimentary Autonomous Commuter Shuttle a,∗, Bernhard Grueberb b , Hanna Frieseb b , Klaus Bogenbergeraa Florian Dandla,∗ a a Bundeswehr Bundeswehr
University University Munich, Munich, Werner-Heisenberg-Weg Werner-Heisenberg-Weg 39, 39, 85577 85577 Neubiberg, Neubiberg, Germany Germany bb BMW Group (Research), 80788 Munich, Germany BMW Group (Research), 80788 Munich, Germany
Abstract Abstract Job Job opportunities opportunities affect affect the the attractiveness attractiveness of of cities cities significantly. significantly. Since Since finding finding aa place place to to live live near near the the workplace workplace is is difficult difficult in in aa lot lot of of cases, cases, many many people people have have to to commute commute by by car car or or public public transport. transport. Especially Especially commutes commutes by by private private vehicle vehicle cause cause congestion, congestion, emissions emissions and and parking parking pressure. pressure. This This work work aims aims to to design design aa shuttle shuttle service service for for employees employees of of aa large large company company living living in in areas areas with with bad bad public public transportation transportation connections connections to to the the workplace. workplace. In In order order to to compliment compliment public public transport, transport, the the shuttle shuttle service service only only operates operates in in these these areas areas and and offers offers aa new new alternative alternative to to commuting commuting by by private private vehicle. vehicle. The The key key of of any any mobility mobility service service is is to to build build aa very very convenient convenient system system with with short short door-to-door door-to-door journey journey times times and and low low prices. prices. The The illustrated illustrated system system design design respects respects the the flexible flexible working working hours hours of of the the single single commuters. commuters. Using Using commuter commuter data data of of aa research research and and development development center, center, we we design design aa realistic realistic scenario scenario for for aa large large company company in in Munich Munich taking taking the the following following approach: approach: first, first, we we identify identify areas areas where where commuters commuters are are unlikely unlikely to to use use public public transport, transport, then then aa user-friendly user-friendly shuttle shuttle service service is is designed designed and and tested tested via via simulations. simulations. The The shuttle shuttle service service will will offer offer low low journey journey times, times, as as well well as as prices prices in in the the range range of of public transportation and private vehicles (operating costs), if the shuttles drive autonomously. Since it is an open question, public transportation and private vehicles (operating costs), if the shuttles drive autonomously. Since it is an open question, when when autonomous autonomous shuttles shuttles will will be be able able to to drive drive in in mixed mixed traffic, traffic, we we also also evaluate evaluate the the costs costs of of the the proposed proposed shuttle shuttle system system with with drivers. drivers. We We find find that that without without subsidies, subsidies, labor labor costs costs for for drivers drivers increase increase the the shuttle shuttle costs costs to to aa noncompetitive noncompetitive level. level.
© 2019 The Authors. Published by Elsevier Ltd. cc 2018 The Authors. by B.V. 2018 The Authors. Published by Elsevier Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review the scientific committee of 2018 Peer-review under under responsibility ofof thethe scientific committee of the the mobil.TUM 2018 conference. conference. Peer-review underresponsibility responsibilityof scientific committee of mobil.TUM the mobil.TUM18. Keywords: Shuttle Keywords: Shuttle Service; Service; Flexible Flexible Ride-Pooling Ride-Pooling Service; Service; On-Demand On-Demand Mobility Mobility
1. 1. Introduction Introduction The The attractiveness attractiveness of of cities cities is is highly highly dependent dependent on on the the amount amount of of job job possibilities possibilities in in its its area. area. Therefore, Therefore, the the growth growth of of companies companies is is generally generally viewed viewed beneficial beneficial to to aa city. city. However, However, the the cities cities tend tend to to become become denser denser over over time. time. The The traffic traffic infrastructure infrastructure cannot cannot grow grow as as steadily steadily as as the the population. population. Hence, Hence, the the emergence emergence and and intensification intensification of of bottlenecks bottlenecks in in both both public public transportation transportation and and the the street street network network is is the the consequence consequence if if no no countermeasures countermeasures are are conducted. conducted. On On the the one one ∗∗
Corresponding Corresponding author. author. Tel.: Tel.: +49-89-6004-2442. +49-89-6004-2442. E-mail address: address: [email protected] E-mail [email protected]
2352-1465 2018 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. cc 2018 2352-1465 2352-1465 under 2019responsibility The Authors. Published Elsevier of Ltd. Peer-review of the the scientificbycommittee committee the mobil.TUM mobil.TUM 2018 2018 conference. conference. Peer-review underaccess responsibility of scientific the This is an open article under the CC BY-NC-NDoflicense (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the mobil.TUM18. 10.1016/j.trpro.2019.09.043
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hand, large scale projects to extend the infrastructure can be planned ahead of time to increase the supply, on the other hand, a better use of the existing infrastructure also improves the situation. This work focuses on the latter aspect for the case of Munich. BMW operates a large research and development center (FIZ) in the northern part of Munich. BMW announced that it will be expanded in the near future1 . As a consequence, it will generate additional traffic demand. It will be difficult to extend the nearby main streets due to space limitations in the city. Furthermore, the parking pressure will likely increase as well. Additionally, the public transportation system in Munich is mainly built radially with limited tangential connections. For this reason, the stations and trains are at its limits in the city-center and the headways of the main public transportation line for the FIZ cannot be decreased much more in the peak hours. For all these reasons, we look for a solution which can reduce the motorized traffic on the streets and support public transportation. This can be achieved by commuters sharing a single vehicle for their trip to and from work. Due to the flexible working hours, casual carpooling is generally no solution for people from the same region. However, if the matches can be made dynamically and within defined time-constraints, the flexibility of the individuals will hardly be limited and we can hope for a high acceptance rate. 2. Research Objectives The objectives of this study structure the remainder of the paper. After providing some related studies, we define metrics to find an operating area, where the proposed shuttle service can support public transportation rather than competing against it. The next step is to design a shuttle service from a user-convenience point of view in order to aim for a high acceptance rate. Then, an operator scheme to support this design is developed. Finally, the feasibility and implications of the shuttle design is tested via simulations. 3. Related Work Operating a shuttle service requires solving dynamic vehicle routing problems. For a general introduction and review to this research field, we refer to Powell et al. (2012) and Psaraftis et al. (2016). Hyland and Mahmassani (2017) created a taxonomy of shared autonomous vehicle fleet management problems. In their terms, the problem at hand represents a multi-vehicle pickup-and-delivery problem with a dynamic evolution of globally available information, i.e. incoming requests, who except service in explicit time windows. The combination of many users into one single vehicle trip is called ride-sharing. Ride-sharing originally denoted the process of a driver and a passenger sharing part of their trips together. For readers unfamiliar with the topic, we refer to Agatz et al. (2012) and Furuhata et al. (2013), who conducted literature reviews about the topic. Stiglic et al. (2015) illustrated the benefit of access points in ride-sharing systems. Furthermore, the impact of driver and rider flexibility on matching rates was studied by Stiglic et al. (2016). The envisioned shuttle service will take advantage of access points and we will introduce two systems with different user flexibility. Since the introduction of transportation network companies like Uber, it is meaningful to differentiate between exclusive ride-hailing where, similarly to taxis, one passenger is transported from origin to destination by paid drivers, and ride-pooling where there are multiple passengers on board at the same time. The differentiation is especially useful in the case that autonomous vehicles are used for these mobility services, as the exclusive ride-hailing will no longer be ”ride-sharing”. The shuttle service we want to design should in general transport multiple passengers. Hence, we can classify it as ride-pooling. Ride-pooling problems usually suffer from the curse of dimensionality when scaling the problem size. In many studies in literature, e.g. Fagnant and Kockelman (2018); Bischoff et al. (2017), rule-based heuristics limit the possible solution space in order to achieve acceptable computational times while loosing the optimal solution in most cases. Efficient algorithms to create and scan the solution space are necessary in order to find better or even the optimal solutions. Simulated annealing (see e.g. Jung et al. (2016)) or tabu search (see e.g. Farhan and Chen (2018)) are used to scan the solution space, while Santi et al. (2014) present a mathematical framework called ”shareability networks” 1
https://www.press.bmwgroup.com/global/article/detail/T0274947EN
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in order to create the solution space in a very efficient way. They are able to solve large problem instances (e.g. the taxi demand in Manhattan) to study the trade-off between individual passenger discomfort, i.e. waiting and detour times, and the collective benefits of sharing trips among 2 or 3 passengers. Alonso-Mora et al. (2017) introduced a so-called ”RTV-graph” and used parallel programming to create a tractable and computationally efficient framework that is capable of matching up to 10 passengers per vehicle for thousands of vehicles. Our methodology in section 4.4 builds on the idea of an RTV-graph. 4. Design of the Shuttle Service In this work, the shuttles are capable of transporting up to 8 passengers. Generally, a larger amount of smaller vehicles would increase flexibility for users, but reduce the amount of traffic volume that can be replaced. The contrary statements can be made for larger vehicles. The limit of 8 passengers was chosen since a typical German driver’s license allows the transport of 8 passengers and drivers would be necessary for an implementation today as autonomous vehicles are not yet able/allowed to use the complete street network. We divide the task of designing the shuttle service into 3 parts. First, we use commuter data to define an operating area that will help to create a public-transport complimentary system. Then, we emphasize the user perspective and the service quality that they expect. Finally, we briefly introduce our fleet-operation algorithms. There is a multitude of possible designs. In section 4.2 we will illustrate a system, which imposes very strict time constraints. In the subsequent section another system will be introduced, which relaxes those constraints allowing higher user flexibility and thereby higher matching rates. 4.1. Definition of an Operating Area to Support Public Transport The shuttle service primarily is supposed to substitute trips with private vehicles and support public transportation in regions that are not well connected to the FIZ. We define two criteria that a region has to satisfy: • the travel time ratio of using public transportation compared to driving with the private car during peak hours should be above 1.3, • the estimated ratio of people commuting to FIZ with public transportation is less than 30 %. The two criteria are strongly related since more people tend to use a private vehicle if the travel time ratio of public transport is bad. However, it is difficult to get the travel time ratio for a whole area. While it is feasible to get travel time information for some points from the Google API, we use a second data source to gain insight into the public transportation usage in the whole zip-code area. We utilized the number of job-tickets, which are subsidized subscriptions for public transportation, the total number of employees per zip-code area, and the share of public transportation for all FIZ employees from a census to estimate the mode share per region. From these data, we first compute the global relation between job-ticket and public transportation share and use this factor to estimate the ratio in the different zip-code areas. In order to reduce detours and traffic inside residential areas, people are not fetched from home, but from access points. Those were selected manually on arterial roads in or near residential areas such that most people of an area live within 500 meters of the access point and can reach it in less than 5 minutes by foot. The areas satisfying the aforementioned criteria and the created access points are shown in Fig. 1. The nature of the mostly radial public transportation network is decisive for the shape of the operating area along the northern tangential. 4.2. First Design: Strict Time Constraints We envision a system, in which commuters can decide in the morning if they want to use the shuttle service or use another transportation mode. The user announces a request 30 minutes ahead of his earliest departure time using a smartphone app. The announcement process will be included into daily routine over time, e.g. after waking up or during breakfast. Hence, the time between the request and the system response will not be considered as waiting time.
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Fig. 1. Map of Munich. The selected zip-code areas for operation are highlighted in blue. Access points are represented by black points and the FIZ is depicted by the red star.
time of request
Max. response time 15 min
Earliest departure from collection point
Latest departure from collection point
Latest arrival at FIZ
Direct travel time (same as with private vehicle) Buffer for way to collection point 15 min Departure flexibility 10 min
Fig. 2. Strict time constraints for a single user in the first design.
In order to give enough time for the user to reach the access point, the operator responds to the request not later than 15 minutes after the request time. The response time ensures that most critical information is available when final decisions are made and responded to the user. If a match was made, the user is told the departure time at the access point. If this will not be at his earliest departure time, he can at least wait at home until he needs to leave to reach the access point in time. Maximal waiting time and detour time are coupled to be 10 minutes in sum. This approach guarantees a latest arrival time in order to achieve a high planning ability and thereby high user acceptance. Moreover, the largest part of the waiting time can be spent at home. Even the time spent walking to the access point can be compensated by the shuttle vehicles dropping off the users directly at the FIZ entrance. This avoids long searches for free parking lots and long walking times to the office. Fig. 2 depicts the time constraints for a user.
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4.3. Second Design: Relaxing Service Quality Constraints The first design will produce many requests that will not be served, if no match with other requests is possible within the strict time constraints. In the end, service guarantee is more important than waiting a few more minutes in all likelihood. Therefore, we introduce a second design trading waiting time at the user’s home versus service rate: If a user cannot be matched within 15 minutes, the departure time window and the latest arrival time at the FIZ are shifted by 10 minutes. Furthermore, the system has to respond to the request within 10 minutes. We will call this process ”retry” from now on. In this second model, a user will retry until a vehicle is assigned. There are other possibilities to relax the time constraints, increase user flexibility and thereby increasing matching probability. The departure flexibility in Fig. 2 could be extended in general or the detour time could be decoupled from the departure time, to name the most obvious ones. Other measures like decreasing the service time or trading less access points for longer walking distances to those access points could increase the matching rates as well. Since this study is a potential analysis and the ”retry”-scenario fits the original idea the closest, we limit this work to this approach. 4.4. Fleet-Operation The system designs in section 4.2 and 4.3 generate problems that are a mixture of static and dynamic vehicle routing problems. On the one hand, the inflow of information, i.e. new requests, is continuous, stochastic and unknown to the operator as in dynamic problems, on the other hand, the introduction of both response time and departure time windows provide the operator with the necessary information to make nearly optimal decisions about future assignments. Therefore, the operator solves new quasi-static ride-pooling problems every minute. The solution to each problem is a set of assignments: a tour that is assigned to one shuttle, which involves the pickup of several users and ends at the FIZ. An assignment cannot be changed anymore as soon as the response time window for one of the users in the tour expires. All users in this tour will be informed at this time and they do not have to be considered in later time steps. Each ride-pooling problem instance can be very complex and computationally demanding since the problem is NP-hard. Inspired by Alonso-Mora et al. (2017), we split it into three sub-problems, which are still NP-hard, but of much smaller dimension and thereby easier to solve: 1. definition of routes between access points (preprocessing), 2. generation and update of a share-ability graph of all users, 3. assignment of bundles of commuters to vehicles. We elaborate on these tasks in the following. 1) In the preprocessing step, the fastest routes from one access point to all others is computed using the algorithm by Dijkstra (1959) with multiple destinations. To gain alternative routes, the travel time of each link on the the resulting best route between two access points is increased by 50% and a new fastest route is computed using an A* algorithm introduced by Hart et al. (1968). The utilization of predefined routes reduces the complexity of finding the best route between two access points (or an access point and the FIZ) to a linear problem with practically no computation time. 2) At the beginning of every time step, the operator updates the share-ability graph. While Alonso-Mora et al. (2017) build this graph separately for each vehicle located anywhere in Manhattan, this work just builds one single graph. Here, all vehicles are either waiting at the FIZ or serving already fixed tours ending there. Hence, it is sufficient to create all feasible bundles of active requests once and check possible combinations with vehicles in a later step. A ”bundle of request” includes the explicit tour of a hypothetical vehicle, as well as the earliest and latest start, and the amount of kilometers that are saved compared to all passengers driving the direct way to the FIZ in a private vehicle. To gain these information, a traveling-salesman problem has to be solved for each bundle. With the condition that each access point can only appear once in each tour, the resulting traveling-salesman problems can be solved by exhaustive search for the following reasons: the number of requests is rather limited in this study (less than 500 per hour), the vehicle occupancy is limited to 8 passengers, and the number of access points is limited. After checking the share-ability of different users, the vehicle availabilities at the FIZ are checked to create the possible combinations of ”bundles of requests” and vehicles.
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3) Finally, the bundles have to be assigned to vehicles. This problem corresponds to solving the following ILP optimization problem. The utility function reflects the distance that is saved by the combination of requests to a tour: max xik
s.t.
i,k
k
cik · xik
(1)
xik ≤ 1 ∀i
(2)
i
k∈K( j)
xik ≤ 1 ∀ j
xik ∈ {0, 1}
(3) (4)
xik is a binary variable, which is 1 in case vehicle i is assigned to bundle k. Equation (2) ensures that each vehicle cannot be assigned more than once. The set of all bundles including an active request j is denoted by K( j). Hence, equation (3) prevents a request from being assigned multiple times. We choose the ”saved vehicle mileage” as cik , i.e. the sum of the direct distances of all requests in a bundle minus the tour distance. Mind, that this choice will not assign shuttles for single requests, because cik will be negative and equation (3) is not ensuring service. Hence, it is possible that users will not be assigned at this time. We use CPLEX to solve this assignment optimization problem. The problem dimension in this work is small enough to wait for optimal solutions.2 5. Simulation Study We use simulations to gain insights about the feasibility and performance of the previously shown shuttle service designs. The acceptance and success of a real shuttle service can be estimated this way. In reality, such service would still have to be advertised among commuters and there would probably be a start phase that shows little pooling and is rather inefficient. We simulate the system in a leveled off demand state that we assume to be realistic. We limit the simulation to the morning period. As the data show rather similar arrival distribution in the morning and departure distribution in the afternoon, we do not expect new insights or different results by carrying out the simulations for the afternoon. 5.1. Simulation Setup In order to simulate the operation of the shuttle service, it is necessary to create explicit requests (with the information time, origin and destination) from the available aggregated data. In this work, we only simulate the way to the FIZ in the morning hours (05:00 10:00) and create 10 different data sets with a randomized creation process. We assume that the acceptance rate of the offered service will be similar to the city-wide ratio of commuters traveling to work by public transportation, which is approximately 48%. For each zip-code, we randomly distribute this share of commuters on the access points. For the timing of requests, we are fortunate to have data of an arrival time distribution with 5 minute bins at the FIZ. Using an average travel time during the morning hours, we estimate necessary departure time distributions from the different areas. Next, we shift this distribution by another 35 minutes (30 minutes between request time and earliest departure time and 5 minutes as an estimated average of the used departure flexibility). Finally, request times are generated by separate Poisson processes for each time interval. Boarding processes are modeled by the vehicle idling for one minute at the access point. For simplicity, we assume that all users are on time. We expect this assumption to be valid because users receive the information of the departure ahead of time and they do not want to be the reason for their coworkers to be late. 2 Approximately 5% of time steps require computation times longer than 1 minute. This would lead to inconsistencies in a real-life operation. These could be avoided by using approximate solutions for the traveling-salesman or the subsequent assignment problems.
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share of retries per area [%] Untermenzing Unterfoehring Oberschleissheim Obermenzing Oberfoehring Lochhausen Lerchenau Karlsfeld Ismaning Garching Froettmaning Feldmoching Feldkirchen Englschalking Daglfing Aschheim Allach 0
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Fig. 3. Distribution of retry ratios per area.
When matching multiple riders from different locations and checking all time constraints, travel times between the different access points are necessary. We take advantage of the preprocessed routes and update the travel times along the predefined routes every 5 minutes according to the velocities from a traffic micro-simulation model of Munich. Occasionally, there might be faster routes through residential areas, but we expect that avoiding these routes might even be politically beneficial. 5.2. Simulation Results The objectives ”user comfort” and ”high vehicle occupancy” contradict each other. First, detours are necessary for pooling. Second, a short departure time window will reduce the amount of matches. The initial shuttle service design (section 4.2) was a system that offers very high convenience to users. As a first step, we wanted to know how feasible such a service is. Simulations with this design showed a rate of 15.7 ± 0.6 % requests that cannot be matched and served. Even though a matching rate of approximately 84% seems high enough on first glance, we expect that commuters, who use a private vehicle nowadays, will only accept a shuttle service regularly if they can rely on it. Hence, we designed the second system (section 4.3). The previously not served requests mostly translate into requests that will have to retry. The spatial distribution of not served requests per area of the first system is very similar to the distribution of requests who have to retry shown in Fig. 3. Requests per access point and the connectivity to other access points are two main factors determining these ratios. We will only show results of the second design from now on. We decided to keep the first design in this work to show that the design of a shuttle service can be a trial-and-error process using simulations. An indicator for the efficiency of matching is vehicle occupancy. We distinguish two vehicle occupancies. Fig. 4 shows the distribution of the number of people arriving at the FIZ in one shuttle vehicle. The average number of passengers exiting the shuttles at the FIZ is 5.1 ± 0.1. Optimally, the distribution would be skewed more to the right, but the matching possibilities do not allow that. To test the hypothesis that the density of requests per time has a large impact on this value, we redistributed the same amount of requests evenly in the time frame between 6:30 and 8:30. Indeed, the average departures at the FIZ increased to 6.5. Since we expect user convenience and flexibility to be of higher importance for the acceptance of a shuttle service, we did not pursue this idea any further. As described in
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share of tours [%]
20
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0
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tours with X passengers arriving at workplace
Fig. 4. Distribution of vehicles arriving at FIZ with X number of passengers.
section 4.4, the choice of saved vehicle miles traveled as cost function does not allow single requests to be served. This distribution of the occupancy at the arrival at the FIZ is useful to inspect free capacities. This could be used to replace some 8-seaters with smaller vehicles. Furthermore, it shows how much traffic flow towards the parking garages near the FIZ could theoretically be saved. Plotting the vehicle arrivals over time would also be necessary to plan the amount of reserved parking lots in front of the FIZ. For an economic and financial analysis, the km-averaged vehicle occupancy, i.e. the ratio of person kilometers divided by the vehicle kilometers, is the better indicator. All trips from the FIZ to the first stop are empty. Furthermore, not all users who leave the vehicle at the FIZ will generally enter at the first stop. Hence, the km-averaged vehicle occupancy is bound to be less than half of the vehicle occupancy at the destination. The simulations resulted in an km-averaged value of 2.3 ± 0.1, which is much lower than the average occupancy at the FIZ, but of course still an improvement to no pooling at all. Besides the driven kilometers, the emission savings is also depending on the emissions of the shuttle vehicles, private vehicles and public transport, as well as the mode of transport the commuters used previously. Since the topic of emissions is rather sensitive, we just show an example calculation at this point. We assume average CO2 emissions of 190 g/km for an 8-seater as shuttle, 125 g/km for private vehicles and 70 g/km for public transport3 . We assumed that 48% of commuters from each region will use the shuttle service. In the best case, only commuters that previously used private vehicles will switch to the shuttle service. In this case, 26 % of CO2 emissions could be saved by the shuttle service. However, we assume that the shuttle service will attract public transport users even more than private vehicle users. Assuming that all public transport users switch to the shuttle service and the rest of the demand is from previous private vehicle users, only 15 % of CO2 emissions can be saved. The kilometer-averaged vehicle occupancy is the direct link between price per person kilometer and cost per vehicle kilometer in case the service is not meant to make neither profit nor deficit. Using the converted per vehicle-km costs of approximately 0.37 Euro/km from Boesch et al. (2017), the per-km price for users will be in the range of 0.16 Euro/km. This value could be even lower if the employer subsidizes the shuttle service with money that could be saved by a reduced amount of parking lots. Even without any subsidizes, the value of 0.16 Euro/km is in the range of the operating costs of private vehicles of Boesch et al. (2017), while the full costs of private vehicles is more than double (0.40 Euro/km). From this standpoint, the shuttle service could attract both spontaneous users who still own their private vehicle, as well as commuters who 3
https://www.umweltbundesamt.de/themen/verkehr-laerm/emissionsdaten
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door‐to‐door journey time [min] Untermenzing Unterfoehring Oberschleissheim Obermenzing Oberfoehring Lochhausen Lerchenau Karlsfeld Ismaning Garching Froettmaning Feldmoching Feldkirchen Englschalking Daglfing Aschheim Allach 0
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Fig. 5. Comparison of door-to-door travel times per area for public transportation (PT), private vehicle (PV), and shuttle.
e.g. abolish their second private vehicle because of the shuttle service. The shuttle could be even cheaper than public transportation, but autonomous buses could reduce their prices as well. As the implementation with autonomous vehicles is not possible yet, we also calculated prices in case the shuttle service would be operated with drivers. However, drivers would make up the largest part of the costs. Assuming the cost structure from Boesch et al. (2017), the costs per vehicle kilometer would be 2.32 Euro/km. Using the kmaveraged vehicle occupancy, we can derive a price of 1.00 Euro/km for the users. This high price would in all likelihood not be accepted by commuters. Another quantity that will have a large impact on user acceptance is door-to-door journey time. The operator responds the estimated departure time at least 15 minutes ahead of time and the users can adapt their walk to the access point to have a very short waiting time there. Since we assume that the users do not consider the additional time at home as disturbing ”waiting time”, the door-to-door journey time is a good indicator of user satisfaction. It only depends on the detour time and access times. To compare this journey time with private vehicles and public transportation, we assume the following average durations: • • • • • •
walk to private vehicle: 0 minutes find parking lot and walk from parking garage to workplace: 10 minutes walk to access point: 2.5 minutes walk from bus stop in front of FIZ to workplace: 5 minutes walk to public transport stop: 2.5 minutes walk from subway stop to workplace: 5 minutes
Fig. 5 depicts the journey times in the different areas. Mind that a high public transport to private vehicle travel time ratio was a necessity to be part of the operating area. But it is noteworthy that the reduction in walking time due to the better parking spot in front of the FIZ even decreases the average door-to-door journey for some commuters, especially for users in areas closer to the FIZ, where detours are not that probable. It is unclear how these journey times will be perceived by commuters. The time in the shuttle could be used to work, get to know or chat with colleagues from other departments, or just relax. Some commuters might prefer their private space in their own vehicle, though.
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6. Conclusion This work showed the design of a shuttle service for commuters with flexible working hours. The operating area was chosen to compliment public transport in those areas that have a bad public transport to private vehicle travel time ratio and consequently low public transportation usage. The user interaction was designed to allow a very high user convenience while still offering enough planning time, matching flexibility and detour possibilities to allow for pooling to happen. Convenience is defined by flexible working hours, a small detour time of maximal 10 minutes, an early announcement of the departure time at the access point to avoid out-of-home waiting time and a bus stop in front of the office building. The trade-off between strict waiting time constraints and service guarantee was shown by introducing two different example designs. Furthermore, the importance of the demand distribution on vehicle occupancy was confirmed in a scenario that concentrated the same amount of requests on a shorter time window. Although in average 5.1 commuters left the shuttles at the FIZ, the km-averaged vehicle occupancy is only 2.3. A shuttle for commuters will by design drive emptily to the first stop in the morning. Moreover, the stop farthest from the destination will generally be the first stop if only small detours are allowed once passengers are on board. Hence, the ratio between the two occupancy indicators will generally be larger than 2. It might be possible to offer public rides to increase the km-averaged occupancy. There are free seats for the trips that are driven emptily now, i.e. the trips from the FIZ to the first stop in the morning and from the last stop to the FIZ in the afternoon. However, the operator has to consider possible additional delays due to these passengers. Furthermore, we analyzed the costs of the system and prices for users. While the designed shuttle service would be too expensive if operated with drivers, the prices could be as low as 0.16 Euro/km when autonomous vehicles are available and allowed to use the complete street network. This value could be even lower if the employer subsidizes the shuttle service with the money that can be saved by a lower number of parking lots. These prices are probably comparable to public transport, as low as operating costs, and much lower than full costs of private vehicles. The door-to-door journey times also show promising results if the shuttle service can use parking lots in front of the workplace, especially if the search for a free space in a parking garage and the subsequent walk to the workplace take more than 10 minutes. This study is a potential analysis and as such has some limitations. These topics should be addressed in case a real operation is in the planning stage. Since requests are input for the simulations, assumptions regarding demand were necessary. We assumed a demand level reflecting the average public transportation mode share of all commuters of this employer in Munich. It is unclear how the proposed system would be accepted by commuters and the sensitivity to demand should be checked. Stochastic travel times and late user arrivals cause delays in real life that both affect the shuttle operation as well as user satisfaction. On the plus side, the time between a request and the departure at the access point could decrease as the operator could learn usage patterns and make better decisions in shorter time. Finally, approximate solutions to both the traveling-salesman and assignment problems highlighted in section 4.4 would be necessary to avoid inconsistencies.
References Agatz, N., Erera, A., Savelsbergh, M., Wang, X., 2012. Optimization for dynamic ride-sharing: A review. European Journal of Operational Research 223 (2), 295–303. Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., Rus, D., 2017. On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proceedings of the National Academy of Sciences of the United States of America, 462–467. Bischoff, J., Maciejewski, M., Nagel, K., 2017. City-wide shared taxis: a simulation study in berlin. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). pp. 275–280. Boesch, P. M., Becker, F., Becker, H., Axhausen, K. W., 2017. Cost-based analysis of autonomous mobility services. Transport Policy. Dijkstra, E. W., 1959. A note on two problems in connexion with graphs. Numerische Mathematik 1 (1), 269–271. Fagnant, D. J., Kockelman, K. M., 2018. Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in austin, texas. Transportation 45 (1), 143–158. Farhan, J., Chen, T. D., 2018. Impact of ridesharing on operational efficiency of shared autonomous electric vehicle fleet. Transportation Research Part C: Emerging Technologies 93, 310–321. Furuhata, M., Dessouky, M., Ord´on˜ ez, F., Brunet, M.-E., Wang, X., Koenig, S., 2013. Ridesharing: The state-of-the-art and future directions. Transportation Research Part B: Methodological 57, 28–46.
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Hart, P., Nilsson, N., Raphael, B., 1968. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4 (2), 100–107. Hyland, M. F., Mahmassani, H. S., 2017. Taxonomy of shared autonomous vehicle fleet management problems to inform future transportation mobility. Transportation Research Record: Journal of the Transportation Research Board 2653, 26–34. Jung, J., Jayakrishnan, R., Park, J. Y., 2016. Dynamic shared-taxi dispatch algorithm with hybrid-simulated annealing. Computer-Aided Civil and Infrastructure Engineering 31 (4), 275–291. Powell, W. B., Simao, H. P., Bouzaiene-Ayari, B., 2012. Approximate dynamic programming in transportation and logistics: A unified framework. EURO Journal on Transportation and Logistics 1 (3), 237–284. Psaraftis, H. N., Wen, M., Kontovas, C. A., 2016. Dynamic vehicle routing problems: Three decades and counting. Networks 67 (1), 3–31. Santi, P., Resta, G., Szell, M., Sobolevsky, S., Strogatz, S. H., Ratti, C., 2014. Quantifying the benefits of vehicle pooling with shareability networks. Proceedings of the National Academy of Sciences of the United States of America, 13290–13294. Stiglic, M., Agatz, N., Savelsbergh, M., Gradisar, M., 2015. The benefits of meeting points in ride-sharing systems. Transportation Research Part B: Methodological 82, 36–53. Stiglic, M., Agatz, N., Savelsbergh, M., Gradisar, M., 2016. Making dynamic ride-sharing work: The impact of driver and rider flexibility. Transportation Research Part E: Logistics and Transportation Review 91, 190–207.
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mobil.TUM 2018 ”Urban Mobility - Shaping the Future Together” - International Scientific Conference on Mobility and Transport
Design and Simulation of a Public-Transportation-Complimentary Autonomous Commuter Shuttle a,∗, Bernhard Grueberb b , Hanna Frieseb b , Klaus Bogenbergeraa Florian Dandla,∗ a a Bundeswehr Bundeswehr
University University Munich, Munich, Werner-Heisenberg-Weg Werner-Heisenberg-Weg 39, 39, 85577 85577 Neubiberg, Neubiberg, Germany Germany bb BMW Group (Research), 80788 Munich, Germany BMW Group (Research), 80788 Munich, Germany
Abstract Abstract Job Job opportunities opportunities affect affect the the attractiveness attractiveness of of cities cities significantly. significantly. Since Since finding finding aa place place to to live live near near the the workplace workplace is is difficult difficult in in aa lot lot of of cases, cases, many many people people have have to to commute commute by by car car or or public public transport. transport. Especially Especially commutes commutes by by private private vehicle vehicle cause cause congestion, congestion, emissions emissions and and parking parking pressure. pressure. This This work work aims aims to to design design aa shuttle shuttle service service for for employees employees of of aa large large company company living living in in areas areas with with bad bad public public transportation transportation connections connections to to the the workplace. workplace. In In order order to to compliment compliment public public transport, transport, the the shuttle shuttle service service only only operates operates in in these these areas areas and and offers offers aa new new alternative alternative to to commuting commuting by by private private vehicle. vehicle. The The key key of of any any mobility mobility service service is is to to build build aa very very convenient convenient system system with with short short door-to-door door-to-door journey journey times times and and low low prices. prices. The The illustrated illustrated system system design design respects respects the the flexible flexible working working hours hours of of the the single single commuters. commuters. Using Using commuter commuter data data of of aa research research and and development development center, center, we we design design aa realistic realistic scenario scenario for for aa large large company company in in Munich Munich taking taking the the following following approach: approach: first, first, we we identify identify areas areas where where commuters commuters are are unlikely unlikely to to use use public public transport, transport, then then aa user-friendly user-friendly shuttle shuttle service service is is designed designed and and tested tested via via simulations. simulations. The The shuttle shuttle service service will will offer offer low low journey journey times, times, as as well well as as prices prices in in the the range range of of public transportation and private vehicles (operating costs), if the shuttles drive autonomously. Since it is an open question, public transportation and private vehicles (operating costs), if the shuttles drive autonomously. Since it is an open question, when when autonomous autonomous shuttles shuttles will will be be able able to to drive drive in in mixed mixed traffic, traffic, we we also also evaluate evaluate the the costs costs of of the the proposed proposed shuttle shuttle system system with with drivers. drivers. We We find find that that without without subsidies, subsidies, labor labor costs costs for for drivers drivers increase increase the the shuttle shuttle costs costs to to aa noncompetitive noncompetitive level. level.
© 2019 The Authors. Published by Elsevier Ltd. cc 2018 The Authors. by B.V. 2018 The Authors. Published by Elsevier Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review the scientific committee of 2018 Peer-review under under responsibility ofof thethe scientific committee of the the mobil.TUM 2018 conference. conference. Peer-review underresponsibility responsibilityof scientific committee of mobil.TUM the mobil.TUM18. Keywords: Shuttle Keywords: Shuttle Service; Service; Flexible Flexible Ride-Pooling Ride-Pooling Service; Service; On-Demand On-Demand Mobility Mobility
1. 1. Introduction Introduction The The attractiveness attractiveness of of cities cities is is highly highly dependent dependent on on the the amount amount of of job job possibilities possibilities in in its its area. area. Therefore, Therefore, the the growth growth of of companies companies is is generally generally viewed viewed beneficial beneficial to to aa city. city. However, However, the the cities cities tend tend to to become become denser denser over over time. time. The The traffic traffic infrastructure infrastructure cannot cannot grow grow as as steadily steadily as as the the population. population. Hence, Hence, the the emergence emergence and and intensification intensification of of bottlenecks bottlenecks in in both both public public transportation transportation and and the the street street network network is is the the consequence consequence if if no no countermeasures countermeasures are are conducted. conducted. On On the the one one ∗∗
Corresponding Corresponding author. author. Tel.: Tel.: +49-89-6004-2442. +49-89-6004-2442. E-mail address: address: [email protected] E-mail [email protected]
2352-1465 2018 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. cc 2018 2352-1465 2352-1465 under 2019responsibility The Authors. Published Elsevier of Ltd. Peer-review of the the scientificbycommittee committee the mobil.TUM mobil.TUM 2018 2018 conference. conference. Peer-review underaccess responsibility of scientific the This is an open article under the CC BY-NC-NDoflicense (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the mobil.TUM18. 10.1016/j.trpro.2019.09.043
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hand, large scale projects to extend the infrastructure can be planned ahead of time to increase the supply, on the other hand, a better use of the existing infrastructure also improves the situation. This work focuses on the latter aspect for the case of Munich. BMW operates a large research and development center (FIZ) in the northern part of Munich. BMW announced that it will be expanded in the near future1 . As a consequence, it will generate additional traffic demand. It will be difficult to extend the nearby main streets due to space limitations in the city. Furthermore, the parking pressure will likely increase as well. Additionally, the public transportation system in Munich is mainly built radially with limited tangential connections. For this reason, the stations and trains are at its limits in the city-center and the headways of the main public transportation line for the FIZ cannot be decreased much more in the peak hours. For all these reasons, we look for a solution which can reduce the motorized traffic on the streets and support public transportation. This can be achieved by commuters sharing a single vehicle for their trip to and from work. Due to the flexible working hours, casual carpooling is generally no solution for people from the same region. However, if the matches can be made dynamically and within defined time-constraints, the flexibility of the individuals will hardly be limited and we can hope for a high acceptance rate. 2. Research Objectives The objectives of this study structure the remainder of the paper. After providing some related studies, we define metrics to find an operating area, where the proposed shuttle service can support public transportation rather than competing against it. The next step is to design a shuttle service from a user-convenience point of view in order to aim for a high acceptance rate. Then, an operator scheme to support this design is developed. Finally, the feasibility and implications of the shuttle design is tested via simulations. 3. Related Work Operating a shuttle service requires solving dynamic vehicle routing problems. For a general introduction and review to this research field, we refer to Powell et al. (2012) and Psaraftis et al. (2016). Hyland and Mahmassani (2017) created a taxonomy of shared autonomous vehicle fleet management problems. In their terms, the problem at hand represents a multi-vehicle pickup-and-delivery problem with a dynamic evolution of globally available information, i.e. incoming requests, who except service in explicit time windows. The combination of many users into one single vehicle trip is called ride-sharing. Ride-sharing originally denoted the process of a driver and a passenger sharing part of their trips together. For readers unfamiliar with the topic, we refer to Agatz et al. (2012) and Furuhata et al. (2013), who conducted literature reviews about the topic. Stiglic et al. (2015) illustrated the benefit of access points in ride-sharing systems. Furthermore, the impact of driver and rider flexibility on matching rates was studied by Stiglic et al. (2016). The envisioned shuttle service will take advantage of access points and we will introduce two systems with different user flexibility. Since the introduction of transportation network companies like Uber, it is meaningful to differentiate between exclusive ride-hailing where, similarly to taxis, one passenger is transported from origin to destination by paid drivers, and ride-pooling where there are multiple passengers on board at the same time. The differentiation is especially useful in the case that autonomous vehicles are used for these mobility services, as the exclusive ride-hailing will no longer be ”ride-sharing”. The shuttle service we want to design should in general transport multiple passengers. Hence, we can classify it as ride-pooling. Ride-pooling problems usually suffer from the curse of dimensionality when scaling the problem size. In many studies in literature, e.g. Fagnant and Kockelman (2018); Bischoff et al. (2017), rule-based heuristics limit the possible solution space in order to achieve acceptable computational times while loosing the optimal solution in most cases. Efficient algorithms to create and scan the solution space are necessary in order to find better or even the optimal solutions. Simulated annealing (see e.g. Jung et al. (2016)) or tabu search (see e.g. Farhan and Chen (2018)) are used to scan the solution space, while Santi et al. (2014) present a mathematical framework called ”shareability networks” 1
https://www.press.bmwgroup.com/global/article/detail/T0274947EN
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in order to create the solution space in a very efficient way. They are able to solve large problem instances (e.g. the taxi demand in Manhattan) to study the trade-off between individual passenger discomfort, i.e. waiting and detour times, and the collective benefits of sharing trips among 2 or 3 passengers. Alonso-Mora et al. (2017) introduced a so-called ”RTV-graph” and used parallel programming to create a tractable and computationally efficient framework that is capable of matching up to 10 passengers per vehicle for thousands of vehicles. Our methodology in section 4.4 builds on the idea of an RTV-graph. 4. Design of the Shuttle Service In this work, the shuttles are capable of transporting up to 8 passengers. Generally, a larger amount of smaller vehicles would increase flexibility for users, but reduce the amount of traffic volume that can be replaced. The contrary statements can be made for larger vehicles. The limit of 8 passengers was chosen since a typical German driver’s license allows the transport of 8 passengers and drivers would be necessary for an implementation today as autonomous vehicles are not yet able/allowed to use the complete street network. We divide the task of designing the shuttle service into 3 parts. First, we use commuter data to define an operating area that will help to create a public-transport complimentary system. Then, we emphasize the user perspective and the service quality that they expect. Finally, we briefly introduce our fleet-operation algorithms. There is a multitude of possible designs. In section 4.2 we will illustrate a system, which imposes very strict time constraints. In the subsequent section another system will be introduced, which relaxes those constraints allowing higher user flexibility and thereby higher matching rates. 4.1. Definition of an Operating Area to Support Public Transport The shuttle service primarily is supposed to substitute trips with private vehicles and support public transportation in regions that are not well connected to the FIZ. We define two criteria that a region has to satisfy: • the travel time ratio of using public transportation compared to driving with the private car during peak hours should be above 1.3, • the estimated ratio of people commuting to FIZ with public transportation is less than 30 %. The two criteria are strongly related since more people tend to use a private vehicle if the travel time ratio of public transport is bad. However, it is difficult to get the travel time ratio for a whole area. While it is feasible to get travel time information for some points from the Google API, we use a second data source to gain insight into the public transportation usage in the whole zip-code area. We utilized the number of job-tickets, which are subsidized subscriptions for public transportation, the total number of employees per zip-code area, and the share of public transportation for all FIZ employees from a census to estimate the mode share per region. From these data, we first compute the global relation between job-ticket and public transportation share and use this factor to estimate the ratio in the different zip-code areas. In order to reduce detours and traffic inside residential areas, people are not fetched from home, but from access points. Those were selected manually on arterial roads in or near residential areas such that most people of an area live within 500 meters of the access point and can reach it in less than 5 minutes by foot. The areas satisfying the aforementioned criteria and the created access points are shown in Fig. 1. The nature of the mostly radial public transportation network is decisive for the shape of the operating area along the northern tangential. 4.2. First Design: Strict Time Constraints We envision a system, in which commuters can decide in the morning if they want to use the shuttle service or use another transportation mode. The user announces a request 30 minutes ahead of his earliest departure time using a smartphone app. The announcement process will be included into daily routine over time, e.g. after waking up or during breakfast. Hence, the time between the request and the system response will not be considered as waiting time.
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Fig. 1. Map of Munich. The selected zip-code areas for operation are highlighted in blue. Access points are represented by black points and the FIZ is depicted by the red star.
time of request
Max. response time 15 min
Earliest departure from collection point
Latest departure from collection point
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Direct travel time (same as with private vehicle) Buffer for way to collection point 15 min Departure flexibility 10 min
Fig. 2. Strict time constraints for a single user in the first design.
In order to give enough time for the user to reach the access point, the operator responds to the request not later than 15 minutes after the request time. The response time ensures that most critical information is available when final decisions are made and responded to the user. If a match was made, the user is told the departure time at the access point. If this will not be at his earliest departure time, he can at least wait at home until he needs to leave to reach the access point in time. Maximal waiting time and detour time are coupled to be 10 minutes in sum. This approach guarantees a latest arrival time in order to achieve a high planning ability and thereby high user acceptance. Moreover, the largest part of the waiting time can be spent at home. Even the time spent walking to the access point can be compensated by the shuttle vehicles dropping off the users directly at the FIZ entrance. This avoids long searches for free parking lots and long walking times to the office. Fig. 2 depicts the time constraints for a user.
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4.3. Second Design: Relaxing Service Quality Constraints The first design will produce many requests that will not be served, if no match with other requests is possible within the strict time constraints. In the end, service guarantee is more important than waiting a few more minutes in all likelihood. Therefore, we introduce a second design trading waiting time at the user’s home versus service rate: If a user cannot be matched within 15 minutes, the departure time window and the latest arrival time at the FIZ are shifted by 10 minutes. Furthermore, the system has to respond to the request within 10 minutes. We will call this process ”retry” from now on. In this second model, a user will retry until a vehicle is assigned. There are other possibilities to relax the time constraints, increase user flexibility and thereby increasing matching probability. The departure flexibility in Fig. 2 could be extended in general or the detour time could be decoupled from the departure time, to name the most obvious ones. Other measures like decreasing the service time or trading less access points for longer walking distances to those access points could increase the matching rates as well. Since this study is a potential analysis and the ”retry”-scenario fits the original idea the closest, we limit this work to this approach. 4.4. Fleet-Operation The system designs in section 4.2 and 4.3 generate problems that are a mixture of static and dynamic vehicle routing problems. On the one hand, the inflow of information, i.e. new requests, is continuous, stochastic and unknown to the operator as in dynamic problems, on the other hand, the introduction of both response time and departure time windows provide the operator with the necessary information to make nearly optimal decisions about future assignments. Therefore, the operator solves new quasi-static ride-pooling problems every minute. The solution to each problem is a set of assignments: a tour that is assigned to one shuttle, which involves the pickup of several users and ends at the FIZ. An assignment cannot be changed anymore as soon as the response time window for one of the users in the tour expires. All users in this tour will be informed at this time and they do not have to be considered in later time steps. Each ride-pooling problem instance can be very complex and computationally demanding since the problem is NP-hard. Inspired by Alonso-Mora et al. (2017), we split it into three sub-problems, which are still NP-hard, but of much smaller dimension and thereby easier to solve: 1. definition of routes between access points (preprocessing), 2. generation and update of a share-ability graph of all users, 3. assignment of bundles of commuters to vehicles. We elaborate on these tasks in the following. 1) In the preprocessing step, the fastest routes from one access point to all others is computed using the algorithm by Dijkstra (1959) with multiple destinations. To gain alternative routes, the travel time of each link on the the resulting best route between two access points is increased by 50% and a new fastest route is computed using an A* algorithm introduced by Hart et al. (1968). The utilization of predefined routes reduces the complexity of finding the best route between two access points (or an access point and the FIZ) to a linear problem with practically no computation time. 2) At the beginning of every time step, the operator updates the share-ability graph. While Alonso-Mora et al. (2017) build this graph separately for each vehicle located anywhere in Manhattan, this work just builds one single graph. Here, all vehicles are either waiting at the FIZ or serving already fixed tours ending there. Hence, it is sufficient to create all feasible bundles of active requests once and check possible combinations with vehicles in a later step. A ”bundle of request” includes the explicit tour of a hypothetical vehicle, as well as the earliest and latest start, and the amount of kilometers that are saved compared to all passengers driving the direct way to the FIZ in a private vehicle. To gain these information, a traveling-salesman problem has to be solved for each bundle. With the condition that each access point can only appear once in each tour, the resulting traveling-salesman problems can be solved by exhaustive search for the following reasons: the number of requests is rather limited in this study (less than 500 per hour), the vehicle occupancy is limited to 8 passengers, and the number of access points is limited. After checking the share-ability of different users, the vehicle availabilities at the FIZ are checked to create the possible combinations of ”bundles of requests” and vehicles.
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3) Finally, the bundles have to be assigned to vehicles. This problem corresponds to solving the following ILP optimization problem. The utility function reflects the distance that is saved by the combination of requests to a tour: max xik
s.t.
i,k
k
cik · xik
(1)
xik ≤ 1 ∀i
(2)
i
k∈K( j)
xik ≤ 1 ∀ j
xik ∈ {0, 1}
(3) (4)
xik is a binary variable, which is 1 in case vehicle i is assigned to bundle k. Equation (2) ensures that each vehicle cannot be assigned more than once. The set of all bundles including an active request j is denoted by K( j). Hence, equation (3) prevents a request from being assigned multiple times. We choose the ”saved vehicle mileage” as cik , i.e. the sum of the direct distances of all requests in a bundle minus the tour distance. Mind, that this choice will not assign shuttles for single requests, because cik will be negative and equation (3) is not ensuring service. Hence, it is possible that users will not be assigned at this time. We use CPLEX to solve this assignment optimization problem. The problem dimension in this work is small enough to wait for optimal solutions.2 5. Simulation Study We use simulations to gain insights about the feasibility and performance of the previously shown shuttle service designs. The acceptance and success of a real shuttle service can be estimated this way. In reality, such service would still have to be advertised among commuters and there would probably be a start phase that shows little pooling and is rather inefficient. We simulate the system in a leveled off demand state that we assume to be realistic. We limit the simulation to the morning period. As the data show rather similar arrival distribution in the morning and departure distribution in the afternoon, we do not expect new insights or different results by carrying out the simulations for the afternoon. 5.1. Simulation Setup In order to simulate the operation of the shuttle service, it is necessary to create explicit requests (with the information time, origin and destination) from the available aggregated data. In this work, we only simulate the way to the FIZ in the morning hours (05:00 10:00) and create 10 different data sets with a randomized creation process. We assume that the acceptance rate of the offered service will be similar to the city-wide ratio of commuters traveling to work by public transportation, which is approximately 48%. For each zip-code, we randomly distribute this share of commuters on the access points. For the timing of requests, we are fortunate to have data of an arrival time distribution with 5 minute bins at the FIZ. Using an average travel time during the morning hours, we estimate necessary departure time distributions from the different areas. Next, we shift this distribution by another 35 minutes (30 minutes between request time and earliest departure time and 5 minutes as an estimated average of the used departure flexibility). Finally, request times are generated by separate Poisson processes for each time interval. Boarding processes are modeled by the vehicle idling for one minute at the access point. For simplicity, we assume that all users are on time. We expect this assumption to be valid because users receive the information of the departure ahead of time and they do not want to be the reason for their coworkers to be late. 2 Approximately 5% of time steps require computation times longer than 1 minute. This would lead to inconsistencies in a real-life operation. These could be avoided by using approximate solutions for the traveling-salesman or the subsequent assignment problems.
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share of retries per area [%] Untermenzing Unterfoehring Oberschleissheim Obermenzing Oberfoehring Lochhausen Lerchenau Karlsfeld Ismaning Garching Froettmaning Feldmoching Feldkirchen Englschalking Daglfing Aschheim Allach 0
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Fig. 3. Distribution of retry ratios per area.
When matching multiple riders from different locations and checking all time constraints, travel times between the different access points are necessary. We take advantage of the preprocessed routes and update the travel times along the predefined routes every 5 minutes according to the velocities from a traffic micro-simulation model of Munich. Occasionally, there might be faster routes through residential areas, but we expect that avoiding these routes might even be politically beneficial. 5.2. Simulation Results The objectives ”user comfort” and ”high vehicle occupancy” contradict each other. First, detours are necessary for pooling. Second, a short departure time window will reduce the amount of matches. The initial shuttle service design (section 4.2) was a system that offers very high convenience to users. As a first step, we wanted to know how feasible such a service is. Simulations with this design showed a rate of 15.7 ± 0.6 % requests that cannot be matched and served. Even though a matching rate of approximately 84% seems high enough on first glance, we expect that commuters, who use a private vehicle nowadays, will only accept a shuttle service regularly if they can rely on it. Hence, we designed the second system (section 4.3). The previously not served requests mostly translate into requests that will have to retry. The spatial distribution of not served requests per area of the first system is very similar to the distribution of requests who have to retry shown in Fig. 3. Requests per access point and the connectivity to other access points are two main factors determining these ratios. We will only show results of the second design from now on. We decided to keep the first design in this work to show that the design of a shuttle service can be a trial-and-error process using simulations. An indicator for the efficiency of matching is vehicle occupancy. We distinguish two vehicle occupancies. Fig. 4 shows the distribution of the number of people arriving at the FIZ in one shuttle vehicle. The average number of passengers exiting the shuttles at the FIZ is 5.1 ± 0.1. Optimally, the distribution would be skewed more to the right, but the matching possibilities do not allow that. To test the hypothesis that the density of requests per time has a large impact on this value, we redistributed the same amount of requests evenly in the time frame between 6:30 and 8:30. Indeed, the average departures at the FIZ increased to 6.5. Since we expect user convenience and flexibility to be of higher importance for the acceptance of a shuttle service, we did not pursue this idea any further. As described in
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share of tours [%]
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tours with X passengers arriving at workplace
Fig. 4. Distribution of vehicles arriving at FIZ with X number of passengers.
section 4.4, the choice of saved vehicle miles traveled as cost function does not allow single requests to be served. This distribution of the occupancy at the arrival at the FIZ is useful to inspect free capacities. This could be used to replace some 8-seaters with smaller vehicles. Furthermore, it shows how much traffic flow towards the parking garages near the FIZ could theoretically be saved. Plotting the vehicle arrivals over time would also be necessary to plan the amount of reserved parking lots in front of the FIZ. For an economic and financial analysis, the km-averaged vehicle occupancy, i.e. the ratio of person kilometers divided by the vehicle kilometers, is the better indicator. All trips from the FIZ to the first stop are empty. Furthermore, not all users who leave the vehicle at the FIZ will generally enter at the first stop. Hence, the km-averaged vehicle occupancy is bound to be less than half of the vehicle occupancy at the destination. The simulations resulted in an km-averaged value of 2.3 ± 0.1, which is much lower than the average occupancy at the FIZ, but of course still an improvement to no pooling at all. Besides the driven kilometers, the emission savings is also depending on the emissions of the shuttle vehicles, private vehicles and public transport, as well as the mode of transport the commuters used previously. Since the topic of emissions is rather sensitive, we just show an example calculation at this point. We assume average CO2 emissions of 190 g/km for an 8-seater as shuttle, 125 g/km for private vehicles and 70 g/km for public transport3 . We assumed that 48% of commuters from each region will use the shuttle service. In the best case, only commuters that previously used private vehicles will switch to the shuttle service. In this case, 26 % of CO2 emissions could be saved by the shuttle service. However, we assume that the shuttle service will attract public transport users even more than private vehicle users. Assuming that all public transport users switch to the shuttle service and the rest of the demand is from previous private vehicle users, only 15 % of CO2 emissions can be saved. The kilometer-averaged vehicle occupancy is the direct link between price per person kilometer and cost per vehicle kilometer in case the service is not meant to make neither profit nor deficit. Using the converted per vehicle-km costs of approximately 0.37 Euro/km from Boesch et al. (2017), the per-km price for users will be in the range of 0.16 Euro/km. This value could be even lower if the employer subsidizes the shuttle service with money that could be saved by a reduced amount of parking lots. Even without any subsidizes, the value of 0.16 Euro/km is in the range of the operating costs of private vehicles of Boesch et al. (2017), while the full costs of private vehicles is more than double (0.40 Euro/km). From this standpoint, the shuttle service could attract both spontaneous users who still own their private vehicle, as well as commuters who 3
https://www.umweltbundesamt.de/themen/verkehr-laerm/emissionsdaten
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door‐to‐door journey time [min] Untermenzing Unterfoehring Oberschleissheim Obermenzing Oberfoehring Lochhausen Lerchenau Karlsfeld Ismaning Garching Froettmaning Feldmoching Feldkirchen Englschalking Daglfing Aschheim Allach 0
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Fig. 5. Comparison of door-to-door travel times per area for public transportation (PT), private vehicle (PV), and shuttle.
e.g. abolish their second private vehicle because of the shuttle service. The shuttle could be even cheaper than public transportation, but autonomous buses could reduce their prices as well. As the implementation with autonomous vehicles is not possible yet, we also calculated prices in case the shuttle service would be operated with drivers. However, drivers would make up the largest part of the costs. Assuming the cost structure from Boesch et al. (2017), the costs per vehicle kilometer would be 2.32 Euro/km. Using the kmaveraged vehicle occupancy, we can derive a price of 1.00 Euro/km for the users. This high price would in all likelihood not be accepted by commuters. Another quantity that will have a large impact on user acceptance is door-to-door journey time. The operator responds the estimated departure time at least 15 minutes ahead of time and the users can adapt their walk to the access point to have a very short waiting time there. Since we assume that the users do not consider the additional time at home as disturbing ”waiting time”, the door-to-door journey time is a good indicator of user satisfaction. It only depends on the detour time and access times. To compare this journey time with private vehicles and public transportation, we assume the following average durations: • • • • • •
walk to private vehicle: 0 minutes find parking lot and walk from parking garage to workplace: 10 minutes walk to access point: 2.5 minutes walk from bus stop in front of FIZ to workplace: 5 minutes walk to public transport stop: 2.5 minutes walk from subway stop to workplace: 5 minutes
Fig. 5 depicts the journey times in the different areas. Mind that a high public transport to private vehicle travel time ratio was a necessity to be part of the operating area. But it is noteworthy that the reduction in walking time due to the better parking spot in front of the FIZ even decreases the average door-to-door journey for some commuters, especially for users in areas closer to the FIZ, where detours are not that probable. It is unclear how these journey times will be perceived by commuters. The time in the shuttle could be used to work, get to know or chat with colleagues from other departments, or just relax. Some commuters might prefer their private space in their own vehicle, though.
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6. Conclusion This work showed the design of a shuttle service for commuters with flexible working hours. The operating area was chosen to compliment public transport in those areas that have a bad public transport to private vehicle travel time ratio and consequently low public transportation usage. The user interaction was designed to allow a very high user convenience while still offering enough planning time, matching flexibility and detour possibilities to allow for pooling to happen. Convenience is defined by flexible working hours, a small detour time of maximal 10 minutes, an early announcement of the departure time at the access point to avoid out-of-home waiting time and a bus stop in front of the office building. The trade-off between strict waiting time constraints and service guarantee was shown by introducing two different example designs. Furthermore, the importance of the demand distribution on vehicle occupancy was confirmed in a scenario that concentrated the same amount of requests on a shorter time window. Although in average 5.1 commuters left the shuttles at the FIZ, the km-averaged vehicle occupancy is only 2.3. A shuttle for commuters will by design drive emptily to the first stop in the morning. Moreover, the stop farthest from the destination will generally be the first stop if only small detours are allowed once passengers are on board. Hence, the ratio between the two occupancy indicators will generally be larger than 2. It might be possible to offer public rides to increase the km-averaged occupancy. There are free seats for the trips that are driven emptily now, i.e. the trips from the FIZ to the first stop in the morning and from the last stop to the FIZ in the afternoon. However, the operator has to consider possible additional delays due to these passengers. Furthermore, we analyzed the costs of the system and prices for users. While the designed shuttle service would be too expensive if operated with drivers, the prices could be as low as 0.16 Euro/km when autonomous vehicles are available and allowed to use the complete street network. This value could be even lower if the employer subsidizes the shuttle service with the money that can be saved by a lower number of parking lots. These prices are probably comparable to public transport, as low as operating costs, and much lower than full costs of private vehicles. The door-to-door journey times also show promising results if the shuttle service can use parking lots in front of the workplace, especially if the search for a free space in a parking garage and the subsequent walk to the workplace take more than 10 minutes. This study is a potential analysis and as such has some limitations. These topics should be addressed in case a real operation is in the planning stage. Since requests are input for the simulations, assumptions regarding demand were necessary. We assumed a demand level reflecting the average public transportation mode share of all commuters of this employer in Munich. It is unclear how the proposed system would be accepted by commuters and the sensitivity to demand should be checked. Stochastic travel times and late user arrivals cause delays in real life that both affect the shuttle operation as well as user satisfaction. On the plus side, the time between a request and the departure at the access point could decrease as the operator could learn usage patterns and make better decisions in shorter time. Finally, approximate solutions to both the traveling-salesman and assignment problems highlighted in section 4.4 would be necessary to avoid inconsistencies.
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