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Electrification and on-demand services are one of the main driving forces within the current automotive sector. This paper presents an approach to modeling and simulation of on-demand applications on the example of an electric taxi fleet. With regard to the high daily mileage and just the same idle times, the characteristic mobility behavior of tax...
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... following paragraph addresses the basic concept. The chosen methodology is oriented toward the Multiagent Systems Engineering (MaSE) technique [28]. First we define actors and related tasks within the system. Fig. 1 shows a common scenario for the proposed electric taxi fleet simulation system. Main roles are given by customer, driver, vehicle, fleet management agency and further infrastructure facilities, such as stops or charging stations. In a typical pickup and delivery system, a customer requests a mobility service by contacting a fleet ...
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... differences relate to short trip distances. An explanation for this behavior is that all customer orders are handled centrally by a single fleet agency. This way, direct booking requests from the roadside are neglected. Because of the chosen taxi stand based dispatch algorithm, two extra trips to a customer and back to a stop may be introduced. Fig. 10 represents the variation of taxi status shares over one week. The course illustrates the influence of variable customer demand. Table II gives an overview of the chosen electric vehicle concept and charging station configuration. The powertrain concept features a range of 250 km, as we assume a battery capacity of 51.5 kWh and a mean ...
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... range. The number of served customers is at the same level in both scenarios. An electric taxi serves 11.8 (σ = 4.9) orders and an ICE taxi 11.7 (σ = 3.8 km). As a result, orders are dispatched less equally. In total, 311 of 40,995 service requests cannot be fulfilled by the chosen electric fleet. The distance driven per shift drops about 6.3 %. Fig. 11 compares the distance per shift for both scenarios. ...
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... Here, the authors dynamically adjust fleet sizes based on observed numbers of active taxis. Similarly, Jäger et al. (2017) simulate taxis that sample a desired shift duration at their respective operation start time. Long durations are possible to represent two-shift operations. ...
On-demand ride-pooling systems have gained increasing attention in science and practice in recent years. Simulation studies have shown an enormous potential to reduce fleet sizes and vehicle kilometers traveled if private car trips are replaced with ride-pooling services. However, existing simulation studies assume operation with autonomous vehicles, with no restrictions on operational tasks required when the vehicles are operated by manual drivers.
In this article, we simulate and evaluate the operational challenges of non-autonomous ride-pooling systems through driver shifts and breaks and compare their capacity and efficiency to autonomous on-demand services. Based on the existing ride-pooling service MOIA in Hamburg, Germany, we introduce shift and break schedules and implement a new hub return logic to perform the respective tasks at different types of vehicle hubs. This way, currently operating on-demand services are modeled more realistically and the efficiency gains of such services through autonomous vehicles are quantified.
The results suggest that operational challenges substantially limit the ride-pooling capacity in terms of served rides with a given number of vehicles. While results largely depend on the chosen shift plan, the presented operational factors should be considered for the assessment of current operational real-world services. The contribution of this study is threefold: From a technical perspective, it is shown that the explicit simulation of operational constraints of current services is crucial to assess ride-pooling services. From a policy perspective, the study shows the operational challenges of a ride-pooling service with non-autonomous vehicles and the potential of future autonomous services. Lastly, the paper adds to the literature a practical ride-pooling simulation use case based on observed real-world demand and shift data.
... Agent-based electromobility simulations can be implemented within a wide range of simulation frameworks and using various modeling techniques. In the past, such simulations were realized as Monte-Carlo [12], discrete event-based [17,26,27], and, most prominently, microscopic transport simulations [13,14,18,19,[28][29][30][31]. While Monte-Carlo approaches and discrete event-based simulations are commonly realized in the form of custom implementations, authors typically adapt existing simulation frameworks for microscopic transport simulations, due to their complexity. ...
... Hidalgo and Trippe [17], for instance, assume charging to take place whenever an agent parks at a destination with an SOC of below 30% or when their SOC falls below 60% and the parking duration is longer than 2 h. Similarly, Jäger et al. [26,27], Bischoff and Maciejeweski [28], and Zhang et al. [31] stipulate fixed charging thresholds. Threshold-based charging behavior models users who avoid range anxiety and possible car break-downs long before they appear. ...
... In the vast majority of studies with explicit location choice, charging is modeled to take place at the nearest charger as soon as the respective charging behavior calls for recharging [12][13][14]19,28,30,31]. In simulations in which trips cannot be interrupted or in which chargers are available at only a few, dedicated facilities, agents charge at their trip destinations [15,17] or at the nearest charging station after completing their trip [26,27]. The aforementioned studies only consider proximity in the context of choosing a specific charger, and any other potential influences are left out. ...
In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.
... This section describes the related work about the simulation modelling of the autonomous mobile systems and BSS. Jäger et al. (2017) illustrated the simulation testbed for autonomous mobility-on-demand systems using Anylogic simulation tool. This research mainly focused on autonomous mobility -on demand (AMoD) with autonomous shared taxis and provided necessary insights into modelling autonomous vehicles and travellers' interaction in the system. ...
In this modern era of transportation, bike-sharing systems dynamically increased their scope as one of the main mobility components in cities. Conventional bike-sharing systems significantly impact urban mobility, and the users have more interest in the utilization of bikes for short trips. However, the conventional bike-sharing systems have no dynamic rebalancing and less user friendly. To overcome these drawbacks, we proposed a next-generation autonomous bike-sharing system. This paper discusses the concept and system description for on-demand shared-use self-driving bikes service (OSABS). We need to investigate the economic viability and factors influencing the OSABS system. Furthermore, we present the simulation testbed with the technical implementation of the elements of the OSABS system. We demonstrate a typical agent-based simulation model in AnyLogic, representing self-driving cargo bikes, users, and the operational area of the OSABS system within the inner-city of Magdeburg.
... We distinguish between two approaches to fleet simulation. Object-oriented or agent-based models using a discrete-event framework were popularised in autonomous taxi fleet and general logistics simulation [39][40][41][42], but are also applied to bus fleets [14,15,43,44]. All objects in the simulation-vehicles, charging stations, depots, etc.-are simulated simultaneously in a shared environment with a central simulation clock, each object having its own individual state. ...
Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies; however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to reschedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO; however, under some circumstances, the technologies are on par. This emphasises the need for a detailed analysis of the local bus network in order to make an informed procurement decision.
... We distinguish between two approaches to fleet simulation. Object-oriented or agent-based models using a discrete-event framework were popularised in autonomous taxi fleet and general logistics simulation [39][40][41][42], but are also applied to bus fleets [14,15,43,44]. All objects in the simulationvehicles, charging stations, depots, etc. -are simulated simultaneously in a shared environment with a central simulation clock, each object having its own individual state. ...
Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies, however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to re-schedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO, however, under some circumstances, the technologies are on par. This emphasises the need for detailed analysis of the local bus network in order to make an informed procurement decision.
... For the simulation of the car-sharing system, an agent-based, discrete-event simulation approach was chosen. The simulation is written in JAVA and is derived from the fleet-simulation presented in [12]. It allows for the detailed simulation of multiple entities at reasonable runtimes. ...
Today, many cities provide a public transportation system capable of addressing the mobility needs of their citizens, reducing the dependency on private vehicles. Still, there are use cases where transport by private vehicle is more convenient, e.g., excursions to remote areas or the transportation of large goods. In recent years, car-sharing has evolved as an alternative to a privately owned vehicle, whereby people can rent a vehicle on the street for short durations compared to the traditional stationbased car rental options. BeeZero offered a car-sharing service in Munich (Germany) with a fleet of 50 hydrogen-powered vehicles. The business area was split up into several zones, distributed across the city area. The service focused on round-trip rentals, as each vehicle needed to be returned to the same zone where the rental began. This paper presents the development and implementation of an agent-based simulation model of the BeeZero car-sharing service. The model is validated with historical booking data and enables an analysis of the required walking distances to reach a vacant vehicle. By using data of a general travel survey, the potential and limits of the BeeZero carsharing service are evaluated using the simulation model for the city of Munich.
... In other industrialized countries, the number of EVs has also risen considerably [2,3]. Electrification and on-demand services are two of the main driving forces within the current global automotive sector [4]. Increasing urbanization and stricter environmental regulations require a redesign of existing transportation systems [5]. ...
... Agent-based modeling is used when a system of vehicles is researched and the interaction between those vehicles is of interest [21]. Jäger et al. [4] realize an approach investigating the behavior of an electric taxi fleet with a combined approach using stochastic modeling and an agent-based simulation. The modeling results are subject to dynamic decision making. ...
There has been a significant increase in the sales of electric vehicles (EVs) in the United States and abroad in the last few years. Nevertheless, the overall adoption of these vehicles is hindered by range limits of EVs in conjunction with long charging times. In this context, it is essential to determine current energy demands and to predict future demand. This paper presents approaches for predicting energy consumption of EVs and discusses their eligibility for this purpose. Four modeling approaches (i.e., dynamic systems, neural networks, statistical models, physics-based models) have primarily been used in recent literature. In order to predict an EV's energy demand, several modeling techniques are combined to give an accurate prediction of the future energy consumption. For the battery EVs a combination of physics-based modeling and statistical modeling have shown to be an effective and efficient choice.
... A logging module stores simulation and debug information in a database. Further information about its architecture are published in [12]. The platform manages in total three agent types, namely office, taxi, and fleet management. ...
With recent policies such as the Clean Miles Standard in California and Lyft’s announcement to reach 100% electric vehicles (EVs) by 2030, the electrification of vehicles on ride-hailing platforms is inevitable. The impacts of this transition are not well-studied. This work attempts to examine the infrastructure deployment necessary to meet demand from electric vehicles being driven on Uber and Lyft platforms using empirical trip data from the two services. We develop the Widespread Infrastructure for Ride-hail EV Deployment model to examine a set of case studies for charger installation in San Diego, Los Angeles, and the San Francisco Bay Area. We also conduct a set of sensitivity scenarios to measure the tradeoff between explicit costs of infrastructure versus weighting factors for valuing the time for drivers to travel to a charger (from where they are providing rides) and valuing the rate of charging (to minimize the amount of time that drivers have to wait to charge their vehicle). There are several notable findings from our study: (1) DC fast charging infrastructure is the dominant charger type necessary to meet ride-hailing demand, (2) shifting to overnight charging behavior that places less emphasis on daytime public charging can significantly reduce costs, and (3) the necessary ratio of chargers is approximately 10 times higher for EVs in Uber and Lyft compared to chargers for the general EV owning public.
