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ispartof: Proceedings of hEART 2022 ispartof: 10th Symposium of the European Association for Research in Transportation (hEART 2022) location:Leuven date:1 Jun - 3 Jun 2022 status: Published online

On-demand mobility services are promising to revolutionise urban travel, but preliminary studies are showing they may actually increase total vehicle miles travelled, worsening road congestion in cities. In this study, we assess the demand for on-demand mobility services in urban areas, using a stated preference survey, to understand the potential impact of introducing on-demand services on the current modal split. The survey was carried out in the Netherlands and offered respondents a choice between bike, car, public transport and ondemand services. 1,063 valid responses are analysed with a multinomial logit and a latent class choice model. By means of the latter, we uncover four distinctive groups of travellers based on the observed choice behaviour. The majority of the sample, the Sharing-ready cyclists (55%), are avid cyclists and do not see on-demand mobility as an alternative for making urban trips. Two classes, Tech-ready individuals (27%) and Flex-ready individuals (9%) would potentially use on-demand services: the former is fairly time-sensitive and would thus use on-demand service if they were sufficiently fast. The latter is highly costsensitive, and would therefore use the service primarily if it is cheap. The fourth class, Flex-sceptic individuals (9%) shows very limited potential for using on-demand services., Transportation (2022)

With the popularization of transportation network companies (TNCs) (e.g., Uber, Lyft) and the rise of autonomous vehicles (AVs), even major car manufacturers are increasingly considering themselves as autonomous mobility-on-demand (AMoD) providers rather than individual vehicle sellers. However, matching the convenience of owning a vehicle requires providing consistent service quality, taking into account individual expectations. Typically, different classes of users have different service quality (SQ) expectations in terms of responsiveness, reliability, and privacy. Nonetheless, AMoD systems presented in the literature do not enable active control of service quality in the short term, especially in light of unusual demand patterns, sometimes allowing extensive delays and user rejections. This study proposes a method to control the daily operations of an AMoD system that uses the SQ expectations of heterogeneous user classes to dynamically distribute service quality among riders. Additionally, we consider an elastic vehicle supply, that is, privately-owned freelance AVs (FAVs) can be hired on short notice to help providers meeting user service-level expectations. We formalize the problem as the dial-a-ride problem with service quality contracts (DARP-SQC) and propose a multi-objective matheuristic to address real-world requests from Manhattan, New York City. Applying the proposed service-level constraints, we improve user satisfaction (in terms of reached service-level expectations) by 53% on average compared to conventional ridesharing systems, even without hiring additional vehicles. By deploying service-quality-oriented on-demand hiring, our hierarchical optimization approach allows providers to adequately cater to each segment of the customer base without necessarily owning large fleets.

Mobility-on-demand (MoD) systems consist of a fleet of shared vehicles that can be hailed for one-way point-to-point trips. The total distance driven by the vehicles and the fleet size can be reduced by employing ridesharing, i.e., by assigning multiple passengers to one vehicle. However, finding the optimal passenger-vehicle assignment in an MoD system is a hard combinatorial problem. In this work, we demonstrate how the VGA method, a recently proposed systematic method for ridesharing, can be used to compute the optimal passenger-vehicle assignments and corresponding vehicle routes in a massive-scale MoD system. In contrast to existing works, we solve all passenger-vehicle assignment problems to optimality, regularly dealing with instances containing thousands of vehicles and passengers. Moreover, to examine the impact of using optimal ridesharing assignments, we compare the performance of an MoD system that uses optimal assignments against an MoD system that uses assignments computed using insertion heuristic and against an MoD system that uses no ridesharing. We found that the system that uses optimal ridesharing assignments subject to the maximum travel delay of 4 minutes reduces the vehicle distance driven by 57 % compared to an MoD system without ridesharing. Furthermore, we found that the optimal assignments result in a 20 % reduction in vehicle distance driven and 5 % lower average passenger travel delay compared to a system that uses insertion heuristic., Comment: Non-revised version (before first submission). Currently published online in the Journal of Intelligent Transportation Systems

The emergence of the sharing economy in urban transportation networks has enabled new fast, convenient and accessible mobility services referred to as Mobilty-on-Demand systems (e.g., Uber, Lyft, DiDi). These platforms have flourished in the last decade around the globe and face many operational challenges in order to be competitive and provide good quality of service. A crucial step in the effective operation of these systems is to reduce customers' waiting time while properly selecting the optimal fleet size and pricing policy. In this paper, we jointly tackle three operational decisions: (i) fleet size, (ii) pricing, and (iii) rebalancing, in order to maximize the platform's profit or its customers' welfare. To accomplish this, we first devise an optimization framework which gives rise to a static policy. Then, we elaborate and propose dynamic policies that are more responsive to perturbations such as unexpected increases in demand. We test this framework in a simulation environment using three case studies and leveraging traffic flow and taxi data from Eastern Massachusetts, New York City, and Chicago. Our results show that solving the problem jointly could increase profits between 1% and up to 50%, depending on the benchmark. Moreover, we observe that the proposed fleet size yield utilization of the vehicles in the fleet is around 75% compared to private vehicle utilization of 5%.

Autonomous vehicles (AVs) have been heralded as the key to unlock a shared mobility future where transportation is more efficient, convenient, and cheaper. However, the AV utopia can only come to fruition if the majority of users trust that autonomous mobility-on-demand (AMoD) systems are on a par with owning a vehicle in terms of service quality. Once the perception of quality is highly subjective, we propose a more personalized approach to on-demand mobility, in which users are segmented into service quality classes. These classes comprise minimum requirements regarding responsiveness and privacy, allowing us to model a series of user profiles formalized using strict service quality contracts. By honoring these contracts, providers can build users' trust and gain their loyalty, which on a grander scheme can contribute to a faster transition to a shared mobility future.This thesis presents a series of strategies to guaranteeing service quality throughout operational scenarios arising in the timeline of AV technology deployment. First, a precondition to providing service quality in autonomous transportation is safety. During a transition phase to full automation, AV operation will likely be restricted to areas where safe operations are guaranteed, leading to the formation of hybrid street networks comprised of autonomous and non-autonomous vehicle zones. In this setting, meeting user service quality expectations is primarily a matter of coverage, once mobility services will have to access both AV-ready and not AV-ready areas. Accordingly, this thesis proposes solutions to overcome the challenges entailed by such a transition scenario, where infrastructures, regulatory measures, and AV technology are gradually evolving.Then, assuming that widespread automated driving is the new status quo, we set out to model rich autonomous transportation scenarios comprised of heterogeneous users and vehicles. Central to our analysis is finding an adequate tradeoff between fleet size and service quality. In traditional AMoD systems, providers can do only so much to prevent user dissatisfaction since, to some extent, this is a matter of having enough vehicles. When the demand outstrips the supply, users inevitably experience longer delays or even rejections, ultimately undermining trust in the service. However, these shortcomings may plague future transportation systems only if setting the fleet size and mix remains a strategic decision. In contrast to most related literature, this thesis investigates a disseminated AV ownership scenario, where ridesharing platforms can occasionally hire available privately-owned AVs on-demand. In this scenario, customers can simultaneously own and share AVs, a setup that better resembles the operation of today's transportation network companies (TNCs), which rely entirely on micro-operators. As a result, AMoD systems can increase and decrease vehicle supply in the short term, thus shifting fleet sizing to the operational planning level.Moreover, analogously to other transportation modes, we consider that the system must deal with a diversified user base with different service quality expectations. This setup allows providers greater leeway to explore requests' delay tolerances to design efficient routes. To balance user expectations and avoid an oversupply of vehicles, we propose a multi-objective matheuristic that dynamically hires third-party AVs to meet the demand. Our approach adds to recent literature by allowing providers to prioritize different customer segments, besides choosing the exact tradeoff between meeting each segment's needs and hiring extra vehicles. This way, when vehicles are lacking, the optimization process can steer the ride-matching solution towards addressing user requests in order of importance (e.g., most lucrative first). To make the most of currently working vehicles, we also design a repositioning algorithm that fixes supply and demand imbalances using users' service level violations as stimuli.Further, to enable anticipatory decision making, this thesis incorporates the stochastic information surrounding both privately-owned AV supply and heterogeneous passenger demand in the fleet management process. We propose a learning-based optimization approach that uses the underlying assignment problem's dual variables to iteratively approximate the marginal value of vehicles at each time and location under different availability settings. In turn, these approximations are used in the optimization problem's objective function to weigh the downstream impact of dispatching, rebalancing, and occasionally hiring vehicles. By harnessing the historical knowledge regarding both demand and supply patterns, we show that AMoD providers are substantially better equipped to meet user needs without necessarily having to own large AV fleets.Typically, learning-based fleet management strategies end up reinforcing biases present in the demand data, therefore frequently moving towards cities' most affluent and densely populated areas, where alternative mobility choices already abound. Although lucrative for providers, this fleet management strategy runs counter to a broader city goal of equitably distributing accessibility across all regions and population demographics. To counterbalance the demand biases, we investigate the extent to which fare subsidization policies can drive the learning process towards sending vehicles to targeted regions where accessibility is lacking. Our results suggest that by using an adequate scheme of incentives, policymakers can orchestrate transportation providers to diminish the insidious effects of ``cream-skimming'' practices, thus using AVs in favor of mobility equity.Lastly, once we have designed strategies that balance the goals of cities, independent owners, fleet owners, and users, we focus on a different approach to maximizing fleet productivity in urban environments. No matter how efficient a fleet optimization method can be, by limiting AVs to service a single commodity type (i.e., people), fleet utilization and consequently profits are bounded by passenger demand patterns. As autonomous technology evolves, however, new opportunities to improve asset utilization arise. We end this thesis with a model for a versatile transportation system where mixed-purpose compartmentalized AVs can address both passengers and goods simultaneously. With the growth of e-commerce and same-day deliveries, our approach provides a starting point to study more flexible short-haul integration systems to consolidate passenger and freight flows.

Enabling Ride-sharing (RS) in Mobility-on-demand (MoD) systems allows reduction in vehicle fleet size while preserving the level of service. This, however, requires an efficient vehicle to request assignment, and a vehicle rebalancing strategy, which counteracts the uneven geographical spread of demand and relocates unoccupied vehicles to the areas of higher demand. Existing research into rebalancing generally divides the coverage area into predefined geographical zones. Division is done statically, at design-time, impeding adaptivity to evolving demand patterns. To enable more accurate dynamic rebalancing, this paper proposes a Dynamic Demand-Responsive Rebalancer (D2R2) for RS systems. D2R2 uses Expectation-Maximization (EM) technique to recalculate zones at each decision step based on current demand. We integrate D2R2 with a Deep Reinforcement Learning multi-agent MoD system consisting of 200 vehicles serving 10,000 trips from New York taxi dataset. Results show a more fair workload division across the fleet when compared to static pre-defined equiprobable zones.

Technological innovations in recent decades have led to a further decrease in transport costs. As a result, global passenger transport, which is a primary enabler of increased globalisation, is at an all-time high. This has led to interconnectedness of states, organisations and people on a scale never seen before. While this development has many positive sides, such as increased overall prosperity, the transportation sector is also a significant source of greenhouse gas emissions and thus a primary driver of climate change. In parallel, digital transformation is leaving its mark on the transport sector. It is a principal driver of future transport directions, including automated driving, passenger drones and the autonomous delivery of goods. More subtly, today's transport system is already being transformed. New business models that rely on tight digital integration of services have started to appear and new mobility services such as ride sourcing, (e-)bike sharing and e-scooter sharing have been introduced in many cities around the world. Specifically, two developments connected to digital transformation have driven the emergence of new mobility services and have increased analysts' ability to study the transportation system. First, as a consequence of the high market penetration of smartphones, there is a direct interface between most individuals and mobility providers. This allows for the comprehensive personalisation of services: the location of the customer, information about capacities of transport networks and complementary information (such as weather data) can be taken into account when a service is offered. Second, the cost and effort required to obtain data on individuals, including their location, has decreased, enabling this information to be used to inform the design of transport policies. This thesis presents three contributions that are to a great extent driven by the global trends of digital transformation and subsequent personalisation. Two contributions are part of the broad topic of new mobility services, which are likely to shape future (urban) transport systems: Mobility as a Service (MaaS) bundles and electric bicycle-sharing systems. A wide array of shared and highly integrated services may contribute to a decrease in emissions and have the potential to ease the tension between increasing mobility and mitigating the environmental burden of the transport system. The third contribution assumes a more global perspective and examines one of the primary determinants of daily commuting patterns: choice of residential location relative to an individual's work location. A personal network survey was conducted to study the effects of proximity to personal contacts on residential location choice and its connection to commuting. The first contribution of the thesis shows that MaaS bundles may indeed be an attractive option for a certain share of the population. However, the results also suggest that not all services are viewed as complementary by potential customers. Including non-complementary services may decrease the propensity of customers to choose a particular bundle. Operators should thus carefully target bundles to potential customers. The second contribution analyses free-floating e-bike sharing. Free-floating (e-)bike-sharing services are often part of MaaS bundles and provide the flexibility that traditional public transportation sometimes lacks. This chapter shows that free-floating e-bike sharing is part of an ongoing evolution of bicycle-sharing systems. The competitive position of e-bike sharing compared to traditional modes of urban transportation is also examined. Drivers of e-bike-sharing demand are analyzed and it is shown that population, workplace density and proximity to central locations are among the most important drivers of demand. Transferability of the models to new areas is also analysed and discussed. The question of how personal networks affect residential location choice is answered using data from a personal network survey that was collected as part of this thesis. It is shown that personal networks are an important factor and ignoring them may lead to an overestimation of commute time in analyses of residential location choice. Partial compensation of longer commutes may be achieved through higher access to individual social capital that is provided by proximity to personal contacts. This potentially affects transport policies aimed at reducing time spent commuting.

The transport of patients from the inpatient service to the operating room is a recurrent task in a hospital routine. This task is repetitive, non-ergonomic, time consuming, and requires the labor of patient transporters. In this paper is presented a system, named Connected Driverless Wheelchair, that can receive transportation requests directly from the hospital information management system, pick up patients at their beds, navigate autonomously through different floors, avoid obstacles, communicate with elevators, and drop patients off at the designated operating room. As a result, a prototype capable of transporting patients autonomously in hospital environments was obtained. Although it was impossible to test the final developed system at the hospital as planned, due to the COVID-19 pandemic, the extensive tests conducted at the robotics laboratory facilities, and our previous experience in integrating mobile robots in hospitals, allowed to conclude that it is perfectly prepared for this integration to be carried out. The achieved results are relevant since this is a system that may be applied to support these types of tasks in the future, making the transport of patients more efficient (both from a cost and time perspective), without unpredictable delays and, in some cases, safer.

The global market for MoD services is in a state of rapid and challenging transformation, with new market entrants in Europe, such as Uber, MOIA, and CleverShuttle, competing with traditional taxi providers. Rapid developments in available algorithms, data sources, and real-time information systems offer new possibilities of maximizing the efficiency of MoD services. In particular, the use of demand predictions is expected to contribute to a reduction in operational costs and an increase in overall service quality. This paper examines the potential of predictive fleet management strategies applied to a large-scale real-world taxi dataset for the city of Munich. A combination of state-of-the art dispatching algorithms and a predictive RHC optimization for idle vehicle rebalancing was developed to determine the scale by which a fleet size can be reduced without affecting service quality. A simulation study was conducted over a one-week period in Munich, which showed that predictive fleet strategies clearly outperform the present strategy in terms of both service quality and costs. Furthermore, the results showed that current taxi fleets could be reduced to 70% of their original size without any decrease in performance. In addition, the results indicated that the reduced fleet size of the predictive strategy was still 20% larger compared to the theoretical optimum resulting from a bipartite matching approach.

International audience; Public transport lines, especially train lines, have historically played an important role as economic lifelines of rural areas. They are one of the most important factors contributing to economic prosperity as they provide access to mobility for all the inhabitants of these regions. Maintaining such rural public transport lines can be a challenge due to the low utilization inherent to rural areas. Today, with the emergence of fully self-driving cars, on-demand mobility schemes in which autonomous robotic taxis transport passengers, are becoming possible. In this work, we analyze if rural public transport lines with low utilization can be replaced with autonomous mobility-on-demand systems. More specifically, we compare the existing public transportation infrastructure to hypothetical mobility-on-demand systems both in terms of cost and service level. We perform our analysis, which focuses on the operational aspects, using a simulation approach in which unit-capacity robotic taxis are operated in a street network taking into account congestion effects and state-of-the-art control (dispatching and rebalancing) strategies. Our study considers the case of four rural train lines in Switzerland that operate at low utilization and cost coverage. We show that a unit-capacity mobility-on-demand service with self-driving cars reduces both travel times and operational cost in three out of four cases. In one case, even a service with human driven vehicles would provide higher service levels at lower cost. The results suggest that centrally coordinated mobility-on-demand schemes could be a very attractive option for rural areas.

One-way car-sharing systems are becoming increasingly popular, and the introduction of autonomous vehicles could make these systems even more widespread. Shared Autonomous Electric Vehicles could also allow for more controllable charging compared to private electric vehicles, allowing large scale demand response and providing essential ancillary services to the electric grid. In this work, we develop a simulation methodology for evaluating a Shared Autonomous Electric Vehicle system interacting with passengers and charging at designated charging stations using a heuristic-based charging strategy. The influence of fleet size is studied in terms of transport service quality and break-even prices for the system. We test the potential of the system to supply operating reserve by formulating an optimization problem for the optimal deployment of vehicles during a grid operator request. The results of the simulations for the case study of Tokyo show that a fleet of Shared Autonomous Electric Vehicles would only need to be about 10%–14% of a fleet of private cars providing a comparable level of transport service, with low break-even prices. Moreover, we show that the system can provide operating reserve under several operational conditions even at peak transport demand without significant disruption to transport service.

International audience; We evaluate performance of four different operational policies to control an automated mobility-on-demand system with sequential vehicle-sharing in one public open-source-accessible, agent-based, high fidelity simulation environment. Detailed network dynamics on a road level of precision are taken into account. The case study is conducted in a simulation scenario of Zurich city. The results indicate that automated vehicles’ shared mobility systems can provide approximately six times higher occupancy rates than conventional private cars, but that their costs – in Switzerland – are considerably higher than those of subsidized public transport or private cars in the short term. However, these services are predicted to be considerably cheaper than the full costs of owning and using a private car, which makes the long-term adoption of automated taxi services likely. Simulations demonstrate that choice of fleet operational policy determining customer-vehicle assignment and repositioning of empty vehicles (rebalancing) heavily influences system performance, e.g., wait times and cost. This aspect must thus be regarded as an important factor in the interpretation of existing and future simulation studies.

Autonomous vehicles (AVs), but in particular shared autonomous transit on demand (ATOD), promises many efficiencies in future transport provision, and may lead to concomitant changes in urban form. Considering the effects of car-oriented planning on the livability, efficiency and sustainability of 20th century cities, there is growing interest in how we may anticipate the changes that this disruption will bring about. Parking and pick-up drop-off infrastructures are some of the several aspects which may change travel behaviour in the upcoming era of AVs. In the paper, three different parking strategies as well as four types of pick-up drop-off infrastructures are simulated to assess their influence in users and operators. The studied parking strategies include demand-based roaming, parking on the street and parking in depots. The four types of pick-up drop-off interfaces are infinity bay, demand-based bay, curbside and single vehicle. The proposed fleet include 3 vehicle sizes: 4-, 10- and 20-seaters for sharing mobility, and 1-seaters for private mobility. Combinations of different parking strategies and different pick-up drop-off infrastructures were evaluated from the perspective of travel time, walk distance, vehicle occupancy, rejected requests and vehicle kilometers traveled. Results show that strategies produce radically different utilization of vehicles to provide the same minimum service level for a particular study area in Singapore. We conclude that urban designers and policy-makers need to consider these as important parameters when designing or retrofitting neighborhoods if they want to maximize potential benefits from this new transportation mode., TRB Annual Meeting Online

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of ridesharing. We take a multi-objective approach, evaluating 12 metrics related to global efficiency, complexity, passenger, driver, and platform incentives, in settings designed to closely resemble reality in every aspect, focusing on vehicles of capacity two. To the best of our knowledge, this is the largest and most comprehensive evaluation to date. We (i) identify CARs that perform well on global, passenger, driver or platform metrics, (ii) demonstrate that lightweight relocation schemes can significantly improve the Quality of Service by up to $50\%$, and (iii) highlight a practical, scalable, on-device CAR that works well across all metrics., Comment: A version of this paper has been published in the Artificial Intelligence Review (https://doi.org/10.1007/s10462-022-10145-0)

Diese Arbeit behandelt das Begriffs- und Konzeptverständnis von Bürgern in Deutschland in Bezug auf neue, bedarfsorientierte Mobilitätsangebote. Mobilität ist ein immer wichtiger werdendes, präsentes und sehr aktuelles Thema, nicht nur in den Medien, sondern auch politisch und wirtschaftlich. Jedoch verändern sich die Anforderungen der Bürger an die Mobilität und das Nutzungsverhalten stets mit der Zeit, zudem ändern sich auch die infrastrukturellen, politischen und wirtschaftlichen Interessen der Mobilitätsanbieter und der Kommunen und Städte. Die herkömmlichen Verkehrsmittel wie Autos, Busse und Bahnen, Flugzeuge, Schiffe, Fahrräder und Taxis sind jedem ein Begriff. Zudem ergeben sich durch die vielfältigen, unterschiedlichen, komplexen Lebensweisen der Menschen durch die Globalisierung und die verschiedensten Möglichkeiten zur Freizeitgestaltung die Optionen, verschiedene Transportmittel intermodal zu nutzen, um Mobilität möglichst individuell, ökonomisch und angenehm zu gestalten. Dabei kommen immer mehr Angebote von Firmen und Unternehmen auf den Markt, aber auch von Privatpersonen, Städten und Gruppen. Einige halten sich auf dem Markt, andere verschwinden schnell wieder. Zudem werden zur Beschreibung der Services die unterschiedlichsten Begriffe verwendet. Diese sollen in dieser Arbeit gesammelt und definiert werden. In einer Umfrage soll mittels quantitativer Forschung zu einem Ergebnis gekommen werden, welche Überbegriffe für die unterschiedlichen Angebote gefunden werden und wie sie kategorisiert werden können. Zudem soll herausgefunden werden, welche Angebote die Bürger kennen und welche nicht, und was sie sich unter den bislang verwendeten Begriffen vorstellen. Zudem haben die Teilnehmer die Möglichkeit, selbst Überbegriffe vorzuschlagen. Somit sollen folgende Forschungsfragen beantwortet werden: Welche neuen Mobilitätsangebote gibt es im Bereich der bedarfsorientierten Personenbeförderung? Welche appbasierten Mobilitätskonzepte gibt es in Deutschland im Bereich der Shared Mobility und welche Namen, Begriffe und Konzepte verwenden die Mobilitätsanbieter zur Beschreibung des Services? Was verknüpft die Bevölkerung mit den Begriffen? Wie können bestimmte Angebote zusammengefasst werden und was wären sinnvolle Überbegriffe und Definitionen dafür?

Mobilitätsdienstleistungen werden in der heutigen Zeit zunehmend flexibler, moderner und smarter und auf dem Weltmarkt der Mobilitätsanbieter konkurrieren unterschiedlichste Angebote miteinander. Im Rahmen der Literatur und Wissenschaft wurde diese rasante Entwicklung noch nicht vollends thematisiert und bearbeitet. Es fehlen genaue Definitionen, Eigenschaften und Rahmenbedingungen, die Begriffen klar zugeordnet werden können, um die zahlreichen Mobilitätskonzepte voneinander zu unterscheiden. Vor diesem Hintergrund wurden in dieser Arbeit Begriffsdefinitionen erarbeitet, die in der Folge genutzt wurden, um vorgestellte Mobilitätskonzepte voneinander abzugrenzen und zu charakterisieren. Darauf aufbauend wurde eine Medienrecherche durchgeführt, die untersucht welche Begriffe für bestimmte Mobilitätskonzepte genutzt werden. Die resultierenden Ergebnisse haben die unklaren Verhältnisse in der Literatur bekräftigt. Um diese Problematik diskutieren und bewerten zu können, wurden Aufgabenträger, Berater und Wissenschaftler des Verkehrssektors als Experten herangezogen und befragt, um andere Perspektiven auf die Begriffe zu erlangen und die langfristigen Potentiale einer Etablierung der Konzepte zu bewerten.

Die vorliegende Masterarbeit untersucht die Konzeptionierung und iterative Entwicklung eines Serious Games. Das Serious Game (B.u.S.) soll das Konzept eines Mobility-On-Demand Systems (auch genannt Rufbus) vermitteln. Die Entwicklung des Serious Games erfolgt in Zusammenarbeit mit dem Deutschen Zentrum für Luft- und Raumfahrt e. V. (DLR) im Rahmen des Projekts Reallabor Schorndorf. Für die Software-Entwicklung werden iterative Vorgehensmodelle analysiert und ein angepasstes iteratives Vorgehensmodell wird vorgestellt. Dieses iterative Vorgehensmodell beginnt mit der Konzeptentwicklung und berücksichtigt neben einem Fokusgruppengespräch zur Konzept�ndung Usability Tests zur Untersuchung der Benutzerfreundlichkeit. Als Usability Tests werden in dieser Arbeit der Thinking Aloud Test und die Heuristische Evaluation vorgestellt. Die Konzeptentwicklung erfolgt mit Hilfe des Mechanics-Dynamics-Aesthetics (MDA) Frameworks. Alle Ergebnisse der durchgeführten Usability-Evaluationen sind in einen Prototypen des Serious Games einge�ossen, der Prototyp wurde mit Hilfe der Unity-Engine entwickelt. Abschlieÿend stellt diese Arbeit die umgesetzten didaktischen Elemente des Serious Games und den Aufbau der entstandenen Software vor.

In bimodal ridesharing, a private on-demand mobility service operator offers to drop off a passenger at a transit station, where the passenger uses the transit network to get to another transit station, and the service operator guarantees picking up the passenger to drop them off at the final destination. Such collaborations with public transport agencies present a huge potential to increase the ridership. However, most existing studies on dynamic dial-a-ride/ridesharing mainly focus on mono-modal cases only. We consider dynamic bimodal ridesharing problems where real-time information is available to anticipate future demand. A new non-myopic vehicle dispatching and routing policy based on queueing-theoretical approach is proposed and integrated with a nonmyopic idle vehicle repositioning strategy to solve the problem. Several experiments are conducted to test the effectiveness of this integrated solution method and measure the benefit of bimodal cooperation. The proposed model and solution algorithm provides useful tools for real-time operating policy design of shared mobility

Autonomous vehicles (AVs), but in particular shared autonomous transit on demand (ATOD), promises many efficiencies in future transport provision, and may lead to concomitant changes in urban form. Considering the effects of car-oriented planning on the livability, efficiency and sustainability of 20th century cities, there is growing interest in how we may anticipate the changes that this disruption will bring about. Parking is one of the several vehicle behaviors which may be reformed by the upcoming era of AVs. In the paper, four different parking strategies as well as bay constraints are implemented to assess the influence of possible parking regulations on ATOD. Four parking strategies are explored, including demand-based roaming, parking on the street, parking in depots and mixed parking between parking on the street and in depots. We tested bay sizes of 10, 15, 20, 25, and 30m, for a mix of 3 vehicle sizes: 4-, 10 and 20-seaters. Combinations of different parking strategies and different bay sizes were evaluated from the perspective of travel time, walk distance, vehicle occupancy and vehicle kilometers traveled. Results show that strategies produce radically different utilizations of vehicles to provide the same minimum service level for a particular study area in Singapore. We conclude that urban designers and policy-makers need to consider these as important parameters when designing or retrofitting neighborhoods if they want to maximize potential benefits from this new transportation mode., Arbeitsberichte Verkehrs- und Raumplanung, 1370

Kayikci, Yasanur/0000-0003-2406-3164 WOS:000458617100009 Digitization, increasing automation and new business models like shared mobility have revolutionized transportation and mobility. Ridesharing companies like Uber and Lyft provide technological platforms and support to connect drivers and riders on the basis of demand-response services. Although the most improvements in on-demand applications have been experimented in private transit services, there is no any implementation in public transportation to connect public transit services and passengers each other. On-demand is still vague. However, providing on-demand services in public transportation is complicated because of the big capacity problem in mass transit, its application in public transit services can enable flexible mobility for riders and provide personalized mobility experience. This paper presents the concept of mobilityon-demand service and its application in public transit services with an technological innovation of FM/LM pilot project represented by HyperCommute. The paper starts with introduction, then the business model of mobility-on-demand service is described and the most used algorithms are explained, then an illustrative example of HyperCommute mobility-on-demand service is given. Also, the applicability of mobility-ondemand service in Istanbul is discussed. The paper ends up with conclusion and future directions.

The question arises how to satisfy different needs for mobility while at the same time containing traffic, given that future demand for mobility will keep increasing due to urbanization and economic growth. Cities like Oslo, Helsinki, and Madrid have announced their plans to reduce motorized traffic in the next years. Electri-fication, connectivity and automation, as central trends in the upcoming generation of vehicles may not lead to a reduction of traffic but rather increase it. Future mobility is challenged to bundle up traffic demands to handle an increasing mobility demand caused by spatial sprawl, economic growth and flexibilization of work-ing hours. Looking at mobility from a psychological perspective another challenge arises – people strive for a more flexible form of mobility than public mass transport offers. They rather want schedules to adapt to their needs than to plan their mobility regarding to the fixed times of public transport. Demand responsive transport systems offer an opportunity to overcome these challenges for future mobility – both, bundling of demand and flexibility of service. In Germany those dial-a-ride services, called “Anrufbusse” or “Rufbusse”, could serve as a possible guarantor for the preservation of personal mobility, especially in sparsely populated rural areas. Those systems offer public mobility on demand within given temporal and spatial restrictions. Currently, the annual number of passengers of those public on- demand busses is relatively small, especially the amount of deliberate passengers, who decide to use dial-a-ride services despite of other individual mobili-ty options like an own car. Given the hypotheses that dial-a-ride systems are a promising way to handle the challenges of bundling of demand on the one hand and individual flexibility on the other hand also for urban areas, those systems need to be examined more closely. Existing dial-a-ride systems in Germany were ana-lyzed with the aim of identifying factors that influence the use and the success of such systems. Factors were identified by means of an extensive investigation of secondary data as well as interviews with system opera-tors and users. Given the assumption that actual dial-a-ride systems are the antecedents of autonomous public mobility-on-demand systems, like “Robo-Cabs”, this study takes a first step to analyze the predictors of a future efficient and sustainable mobility.