"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:017e26b5-710e-4ad1-b0ea-11628de40272","http://resolver.tudelft.nl/uuid:017e26b5-710e-4ad1-b0ea-11628de40272","Research on Ride-hailing Pricing Strategies","Cui, Kairui (TU Delft Civil Engineering & Geosciences)","Cats, O. (mentor); de Ruijter, A.J.F. (graduation committee); Delft University of Technology (degree granting institution)","2022","Compared to traditional public transport, ride-hailing makes it possible for people to get a more comfortable and faster riding experience with a higher fare. Ride-sharing fall in between the two, offering a discount at the price level of ride-hailing, yet operates with more detours and less comfortable experience. In this study, with different price levels for ride-hailing and discount rates for ride-sharing, we would like to examine the system performance of co-existence of ride-hailing, ride-sharing and public transport services. We would also like to search for an optimal solution for the ride-hailing & ride-sharing company to maximize its profit. We apply ExMAS, an open-source agent-based model for ride-sharing simulation, to simulate passengers' and vehicles' behavior on a microscopic level, and acquire numbers of results. Based on our model, in the case of Amsterdam, when price level is 1.1 euro/km and discount rate is 0.4, the company could enjoy maximum profit and market share. It is also found that, when price level gets higher more people opt for the competitive mode instead, resulting in the overall profit falling significantly.","ride-sharing; ride-hailing; Agent-based model; On demand mobility","en","student report","","","","","","","","","","","","Civil Engineering | Transport and Planning","CIE5050-09 Additional Graduation Work, Research Project",""
"uuid:91dc7e16-e9e0-4674-b9c0-6e3e7eac3a3a","http://resolver.tudelft.nl/uuid:91dc7e16-e9e0-4674-b9c0-6e3e7eac3a3a","Modelling and Assessment of an Autonomous Ride-Sharing Service’s Urban Utilization: Case Study - Rotterdam","Sharifi, Mahtab (TU Delft Technology, Policy and Management; TU Delft Engineering, Systems and Services; TU Delft Transport and Logistics)","Annema, J.A. (mentor); van Daalen, C. (mentor); Führer, K.J. (mentor); Delft University of Technology (degree granting institution)","2022","Increasing demand for passenger services in densely populated urban environments, are currently covered overwhelmingly by private vehicles. Their impact on CO2 emission, present a serious obstacle to the reduction objectives, in the Netherlands alone the target of 45% by 2030, for limiting the global warming to 1.5°C degrees. Autonomous Vehicles (AV) and Ride-Sharing services are believed to be offering a crucial technological and perception shifts to reducing emission. In this work, a methodology for assessing the impact of a large-scale AV fleet ride-sharing system to replace the one-two passenger vehicle traffic using Rotterdam as the case study is designed and proposed.
The approach includes three stages: 1. Building and finetuning a traffic model using publicly available data 2. Designing and implementing a trip merging component, in the form of two distinct heuristic greedy algorithms and a variation of the second one, using Python programming language. 3. Evaluating the impact of each merging scenario on the network in SUMO.
The system’s influence and results are driven from the deployment of the ride-sharing service on the 2016 traffic model. The decrease in total number of trips, vehicle kilometres travels, and subsequent improvements in traffic flow resulted in 39% reduction in CO2 emission with the third algorithm. This result not only establishes the extent of AV ride-sharing service’s potential for emission reduction and traffic quality improvement. This adaptable methodology also operates as a proof of concept for a preliminary step for policy makers when considering implementing such service in any urban setting. Two of the major elements not included in this research are multimodal travel, like combination with public transport, and changes in demand for each mode choice based on traveller’s behaviour. These elements thus remain open for future consideration.","Ride-sharing; CO2 emission reduction; Traffic Simulation","en","master thesis","","","","","","","","","","","","Engineering and Policy Analysis","","51.926517, 4.462456"
"uuid:3e9426a7-a3ec-4943-af7c-55a26592beaa","http://resolver.tudelft.nl/uuid:3e9426a7-a3ec-4943-af7c-55a26592beaa","The effects of pricing and service configurations on a ride-pooling service with pick-up and drop-off points","Maričić, Marko (TU Delft Civil Engineering and Geosciences; TU Delft Transport and Planning)","Cats, O. (mentor); Kucharski, R.M. (graduation committee); Bombelli, A. (graduation committee); Danda, S.R. (graduation committee); Delft University of Technology (degree granting institution)","2021","Ride-pooling is a key concept for the future of human mobility and vital in the roll-out of Mobility-as-a-Service (MaaS). Pooling allows individuals to travel at a reduced fare (when comparing to a private alternative) due to the reduction in operating costs as the usage of the vehicles is increased; essentially, the main intentions of pooling are to alleviate traffic congestion, reduce required operator fleet size, and to reduce vehicle hours travelled. Ride-pooling does have its drawbacks, travellers increase their in-vehicle travel times due to the detours induced by sharing. A way of minimising the time lost due to detouring is to incorporate pick-up and drop-off (PUDO) points that travellers would walk to and from when opting for ride-pooling. However, the knowledge on the extent of these benefits with respect to the service configurations used is still limited, therefore the objective of this thesis is to examine how pricing configurations and service settings affect the operator performance and level of service of ride-pooling with PUDO. We extend the utility formulation of an existing algorithm that matches trip requests to attractive shared rides where a route search algorithm assesses a PUDO configuration of a ride by computing and comparing the utility of the vehicle and the utilities of the travellers within the vehicle. The algorithm is applied to the context of Amsterdam and aims to further optimise selected pooled rides where experimentation consists of varying the door-to-door pooling discount, the PUDO discount, the service setting, and the demand level. The service setting sets weights on the utilities where we can favour the vehicle during the route search or treating the travellers and vehicle equally. Results show that increasing PUDO discount increases general attractiveness of the service allowing for more travellers to opt for ride-pooling with PUDO, however the largest differences in system-wide performance occurred when PUDO discount was significantly larger than door-to-door discount. Total vehicle hours could be reduced up to 2.2%, improve passenger utility by 2.8% but can suffer a loss of revenue up to 11.4%. The service setting was also able to control these performance indicators as favouring the vehicle provided the largest reductions in vehicle hours and lowest loss in revenue while treating the traveller and vehicle equally was able to provide the largest improvement in traveller utility. The former service setting induces longer walking times on travellers which is the cause of the greater reductions in vehicle travel time while the latter service setting is the opposite and the reason to why travellers find it more attractive. In essence, ride-pooling with PUDO is able to further reduce vehicle hours and improve traveller utility; pricing configurations and service settings can be helpful with scenarios where supply exceeds demand and vice versa. The use of the service setting showed that ride-pooling with PUDO can be made much more attractive to travellers by setting fair PUDO points to walk to by sacrificing vehicle travel time savings. Such a traveller orientated service setting could be useful when supply exceeds demand. A service provider such as Uber could utilise the insights obtained from this thesis when rolling out such a service in Amsterdam and plan for certain scenarios.","Ride-pooling; Ride-sharing; Pick-up and drop-off points; Ride-hailing; Walking","en","master thesis","","","","","","","","","","","","Civil Engineering | Transport and Planning","","52.3676,4.9041"
"uuid:78405964-8768-4d33-af32-ba0a02841316","http://resolver.tudelft.nl/uuid:78405964-8768-4d33-af32-ba0a02841316","A Scheduling Model for Aerial Ride-Sharing Operations: with Limited Infrastructure Capacity","van Sunten, Ralph (TU Delft Aerospace Engineering)","Bombelli, A. (mentor); Delft University of Technology (degree granting institution)","2021","Following the advent of drones for surveillance and cargo delivery purposes, advancements in recent years have also been made towards the development of larger drones for passenger transport. The concept of Urban Air Mobility (UAM) prospects to offer ride-sharing services within and between cities. While trip scheduling and vehicle routing algorithms exist for various forms of road-based transportation services, UAM operations pose specific constraints and requirements that, to the best of our knowledge, have not been addressed comprehensively in academic literature. The purpose of this research is to develop a Mixed-Integer Linear Programming (MILP) model that optimally matches the available VTOL (Vertical Take-off and Landing) vehicle fleet with customer trip requests, subject to (UAM-specific) constraints, and which subsequently provides a vehicle’s routing. The model, in particular, addresses the constraint of limited ground infrastructure capacity. A case study is performed where multiple demand distribution scenarios, resembling different use cases, are applied to the model. Results show that wider time windows do not have a clear beneficial effect on the profit and that customer demand distribution has an impact on the efficiency of the operation. Additionally, the results enable the identification of various key input parameters for infrastructure, fleet size and vehicle technology that can improve the overall operation.","Urban Air Mobility; Ride-sharing; vertical takeoff and landing; Vehicle Routing Problem","en","master thesis","","","","","","","","","","","","Aerospace Engineering","",""
"uuid:0785eb19-7325-4036-a38e-ed15b05fc14f","http://resolver.tudelft.nl/uuid:0785eb19-7325-4036-a38e-ed15b05fc14f","Dynamic request assignment in aerial ride sharing operations","Nikolakopoulos, Konstantinos (TU Delft Aerospace Engineering)","Bombelli, A. (mentor); Pavel, M.D. (graduation committee); Roling, P.C. (graduation committee); Delft University of Technology (degree granting institution)","2021","The concept of Urban Air Mobility (UAM) services was created mainly in response to traffic congestions. In this research we focus on UAM services such as those provided by Uber Elevate. We therefore present a framework to solve the Urban Air Mobility Problem with Time Windows (UAMP-TW) under dynamic demand, using an Adaptive Large Neighborhood Search (ALNS) algorithm. The objective of this study is to maximize the operational profit and consider customer satisfaction. Satisfaction is measured by two factors: (1) deviation from desired departure time to actual departure time and (2) deviation from nominal trip duration to actual trip duration. In our analysis we aim to determine a relationship between customers and their contribution towards profit. We address this by running simulation instances that cover three operational scenarios: a morning and evening commuter transportation case (scenarios 1 and 2) and the an occurrence of an event at a specific location (scenario 3). Multiple simulation runs indicated stability, for all three instances, due to low variation of the profit from the mean. A sensitivity analysis on the customers' time-window lengths, satisfaction factors and types concluded that customers with higher time-window lengths are more profitable since it is easier to share-rides with other users. The analysis also showed that when the satisfaction factors have a higher weight in the deviation from the departure time than the trip duration, the overall customer satisfaction is increased together with the profit and the percentage of customers who share rides. Scenario 1 has a higher rate of rebalancing empty vehicles because most requests are generated in the suburbs while the depot is located downtown. This leads to a lower vehicle deployment. In scenarios 2 and 3, most requests are generated downtown and thus more vehicles are deployed. Under dynamic demand, the algorithm has an acceptance rate of new requests of about 90% while a penalty is given to customers who cancel a ride. Analysis showed that customers are rejected if an empty vehicle has to rebalance to their location unless they are premium. In terms of the computational efficiency the algorithm is able to handle between 40-50 requests simultaneously.","Adaptive large neighborhood search; optimization; Ride Sharing; Urban Air Mobility; metaheuristics; Dynamic","en","master thesis","","","","","","","","","","","","Aerospace Engineering | Air Transport and Operations","",""
"uuid:e583b9c7-cd3e-40f0-8b14-6d4c5b4e35f4","http://resolver.tudelft.nl/uuid:e583b9c7-cd3e-40f0-8b14-6d4c5b4e35f4","Shared Mobility-on-Demand Systems: Flattening the Service Level Distribution","Schuller, Pieter (TU Delft Mechanical, Maritime and Materials Engineering)","Alonso Mora, J. (mentor); Fielbaum Schnitzler, A.S. (graduation committee); Delft University of Technology (degree granting institution)","2021","In recent years, Shared Mobility-on-Demand systems have emerged as a great method for door-to-door transportation. Studies have shown that it is possible to route vehicles and assign requests to vehicles efficiently in large-scale systems. These studies commonly report one-dimensional performance metrics such as average vehicle occupancy, service rate, or average waiting time. We repeated a case study using a state-of-the-art Fleet Management Framework and focused on the distribution of the service level over the operation area. We observed that the chance of receiving service in a low demand area was much higher than in a high demand area. Going from this observation, this research’s objective was to research how the state-of-the-art framework can be adjusted such that the rejection rates are more evenly spread over the operation area. We developed different methods that adjust the decision of which mobility requests are serviced or which trips are selected. The methods work such that a request located in an above-average rejection rate area has an increased chance of being serviced. Similarly, a trip that goes through an area of above-average rejection rate also has priority. We set up a Discrete Event Simulation that simulates a Shared Mobility-on-Demand system to research the effects of our added method compared to the original framework. We simulated an artificial city and New York City. The Gini index was used to measure how evenly the rejection rates were spread over the operation area. In many cases, our methods were able to lower both the average rejection rate and the Gini index. With this work, we showed that the state-of-the-art framework’s objective can be extended to a broader goal. This opens up new possibilities to tune the system to match specific transportation needs in different areas of a city.","ride-sharing; gini index; mobility-on-demand; fleet management","en","master thesis","","","","","","","","","","","","Mechanical Engineering | Systems and Control","",""
"uuid:559282f4-5343-4993-b18c-a1fbb6006b53","http://resolver.tudelft.nl/uuid:559282f4-5343-4993-b18c-a1fbb6006b53","Spatial disparities in operator performance and attractiveness of ride-pooling in Amsterdam","Maričić, Marko (TU Delft Civil Engineering and Geosciences)","Kucharski, R.M. (mentor); Cats, O. (mentor); Delft University of Technology (degree granting institution)","2021","Despite its potential benefits of reduced traffic congestion and discounted trips, incorporating ride-pooling in a city comes with a set of challenges that require thorough analysis, optimisation, and planning. Even though, services like \textit{Uber} have existed in Amsterdam for over a decade, city wide ride-pooling has yet to be implemented. This paper uses an algorithm for exact matching of attractive shared rides (ExMAS) and Albatross travel demand data to map and analyse the spatial disparities of key performance indicators of a ride-pooling service in Amsterdam and discover the potential of certain areas in the city. The experiments utilised a set of increasing discounted fares for ride-pooling with increasing travel demand levels. A ride-pooling service with higher discounted fares generally increased the attractiveness of the system and reduced the total vehicle hours, when compared to its non-shared counterpart. It was found that the largest vehicle hour reduction were in areas on the periphery of Amsterdam (namely the West, North, and East areas) where rides of higher degree and longer trips lengths were more likely. However, the user attractiveness of the system tended to be higher in central areas of the city where trip density was higher, trip length shorter, and ride degrees lower. The study also determined that variance of the vehicle hours and user attractiveness decreased and stabilised with increasing demand level. This paper could be a starting point in optimising the possible roll out schemes for a ride-pooling service in Amsterdam.","ride-sharing; ride-pooling; Amsterdam; performance; Spatial analysis","en","student report","","","","","","","","","","","","Civil Engineering | Transport and Planning","",""
"uuid:89c4d6d8-bf17-47e2-8a73-37219dd94ae1","http://resolver.tudelft.nl/uuid:89c4d6d8-bf17-47e2-8a73-37219dd94ae1","Ride-Sharing in an Autonomous Future: Relational Service Design for Autonomous Vehicle Ride-Sharing","Schalkers, Emma (TU Delft Industrial Design Engineering)","Snelders, H.M.J.J. (mentor); Kleinsmann, M.S. (graduation committee); Eikelenberg, Nicole (graduation committee); Koenders, Jan (graduation committee); Delft University of Technology (degree granting institution)","2019","Changing mobility Mobility is one of the fundamentals of our society and for the first time since the introduction of the automobile in the 1900s, we face a disruptive change in our mobility ecosystem. The rise of autonomous technology will allow us to get from a to b while being able to do other activities on the go. Challenges in change Although autonomous mobility will go hand in hand with many benefits, we still have challenges to overcome if we want to implement autonomous vehicles to their fullest potential. If we want to get rid of all the mobility problems we have today, such as congestion, traffic accidents, air pollution and parking limitations, autonomous vehicle rides should be shared. Ride-sharing will allow a larger part of our population to join the autonomous revolution, contributing to Ford’s goal to democratize mobility and increase the ease and speed of implementation in society. Ride-sharing For this to succeed, it is essential to understand peoples motivations to share (or not share) rides. Today’s mobility landscape in San Francisco allowed me to research current ride-sharing concepts. Here I experienced ride-sharing myself and interviewed relevant actors in the servicescape, to find that interpersonal contact is a substantial differentiator in the ride experience. Another main finding revolves around the drivers, whom we are trying to eliminate as we are moving towards an autonomous future. The roles the drivers take on besides enabling transport, bring essential values to the user experience. Scope After gathering extensive user insights during field research in San Francisco and learning about the potential positive effects of AV ride-sharing, I scoped the project to the daily commute in the Netherlands. This use case holds excellent potential for business, but more importantly, has the duration and frequency to make it worth to invest in the interpersonal relations amongst users. As well as diminishing the negative effects of human-driven vehciles (HDVs) on the daily commute in society. Acting-out By co-creating and acting-out shared concepts for the daily commute, I further explored the values and desires of future users. This has led to many insights, design qualities and a lot of funny moments. The raw insights are assembled in the additional deliverable: Session Booklet. The central findings are taken to the synthesis phase. Synthesis To bring all these insights together and make them communicable to Ford, this thesis holds multiple deliverables. Starting with the following vision statement: Autonomous Vehicle rides should be shared to maximally utilise the potential AVs have to offer to society. To successfully design shared AV rides for the daily commute, the service provider should gain individual insights to facilitate a common understanding amongst co-riders & provide a sense of control for each user. This vision is visualized in a communicative drawing, showing a little bit, about a lot information. Since the user insights go much deeper than what can be shown in the drawing, a set of criteria for designing shared AV rides for the daily commute, is created too, showing a lot about a little bit. The criteria are accompanied with a user narrative. This narrative shows how users will experience their daily commute in an AV ride-sharing service, that is designed accordingly.","Autonomous Vehicles; Ride-Sharing; Service Design; Relational Design; Mobility","en","master thesis","","","","","","","","2021-06-25","","","","Strategic Product Design","University Research Project Ford",""
"uuid:5a8bc3d2-fe0c-4eca-8f74-9288b7ae83eb","http://resolver.tudelft.nl/uuid:5a8bc3d2-fe0c-4eca-8f74-9288b7ae83eb","Effect of behavioral and service attributes and distribution of demand on ride-sharing efficiency and level of service","De Ruijter, Arjan (TU Delft Civil Engineering & Geosciences)","Cats, O. (mentor); Alonso Mora, J. (graduation committee); Hoogendoorn, S.P. (graduation committee); Delft University of Technology (degree granting institution)","2019","Previous studies on ride-sharing potential have neglected that ride-sharing choice considers a trade-off of disbenefits with a financial reward. This study considers delays and psychological discomfort related to sharing a vehicle as the main ride-sharing costs. Next to finding how these (possibly heterogeneous) behavioral preferences and the chosen discount structure affect the efficiency and level of service of ride-sharing, the effect of the directionality in demand is also considered. A graph-based approach is applied to allow for efficient assignment of vehicles to requests. The model is tested on an experiment representing an urban context. It was found that ride-sharing potential is strongly dependent on the willingness to share and somewhat less strongly on the delay tolerance. The expected level of service is poorer when sharing preferences vary across the population. Implementation of a ride-sharing service is most successful when directionality in demand is low, while ride-based discounts can be effective in maximizing its societal benefits.","Ride-sharing; Willingness to share; Delay tolerance; Demand distribution; Pricing mechanism; Heterogeneity","en","master thesis","","","","","","","","","","","","Transport, Infrastructure and Logistics","",""
"uuid:0f78c996-59d9-48b0-9280-243644811117","http://resolver.tudelft.nl/uuid:0f78c996-59d9-48b0-9280-243644811117","Automated Pricing Suggestions","Katzy, Jonathan (TU Delft Electrical Engineering, Mathematics and Computer Science); Rietveld, Tim (TU Delft Electrical Engineering, Mathematics and Computer Science); van der Steeg, Jaap-Jan (TU Delft Electrical Engineering, Mathematics and Computer Science); Wiegel, Erik (TU Delft Electrical Engineering, Mathematics and Computer Science)","van Riemsdijk, Birna (mentor); Wang, Huijuan (graduation committee); Dorresteijn, Stefan (graduation committee); Bloo, Roel (graduation committee); Jonker, Catholijn (mentor); Delft University of Technology (degree granting institution)","2018","As Machine Learning is becoming more accessible to small businesses, thanks to the rapid advance in computing power, smaller start-ups such as Sjauf (a ride sharing start-up) are starting to get interested in implementing Machine Learning solutions in their product. Sjauf needed a system that could automatically tell its customers how much a certain trip would cost them. Using this information multiple different models were developed and integrated into an ensemble. This ensemble as well as the models used by it were then used for price prediction. This project is a proof of concept to show that Machine Learning is capable of solving this problem in real time.
After researching state of the art Machine Learning models for price recommendation, the architecture of the system was designed. The supplied data was preprocessed, after which a custom Genetic Algorithm was developed for optimising models and ensembles. After validation on real-life company data, a comparison using empirical metrics was conducted. We use these empirical metrics to show that a bagging ensemble is the most efficient and accurate model for this purpose. This bagging ensemble outperformed the currently implemented functions, whilst adhering to the set boundaries on response times. Lastly, recommendations are made to the company with an overview of potential future work in this subject.
The optimisation of vehicle routes for a MoD fleet is a complex task, especially when allowing for multiple passengers to share a vehicle. Recent studies have presented algorithms that can optimise routes in real-time for large scale ride-sharing systems, but have left opportunities to further enhance fleet performance. The redistribution of idle vehicles towards areas of high demand and the utilisation of high capacity vehicles in a heterogeneous fleet has received little attention. This work presents a method to continuously redistribute idle vehicles towards areas of expected demand and an analysis of fleets with both buses and regular vehicles. Furthermore, a method is proposed to optimise vehicle routes while taking into account vehicle capacities and the future locations of vehicles in anticipation to predicted demand.
In simulations with historical taxi data of Manhattan, 99.8% of transportation requests can be served with a fleet of 3000 vehicles with an average waiting time of 57.4 seconds, and an average in-car delay of 13.7 seconds. Compared to earlier work, a decrease in walk-aways of 95% is obtained for 3000 vehicles, with a 86% decrease in average in-car delay and a 37% decrease in average waiting time. For a small fleet of 1000 small busses of capacity 8 still 84.6% of requests can be served with an average waiting time of 141 seconds and an average in-car delay of 269 seconds. In comparison to prior work, a decrease in walk-aways of 15% is obtained, with a 14% decrease in average in-car delay and a 2% decrease in average waiting time. A heterogeneous fleet of 1000 vehicles consisting of 500 buses and 500 regular vehicles using this new approach can serve approximately the same number of passengers as a homogeneous fleet of 1000 buses using earlier presented algorithms.","optimisation; routing; mobility-on-demand; ride-sharing; ride-sourcing; mobility; transport; optimization; Integer Linear Programming problem; ILP; Mixed integer linear programming; MILP","en","master thesis","","","","","","","","","","","","","",""
"uuid:f0cb86de-d6d3-4377-a33c-7c45bf6c9b71","http://resolver.tudelft.nl/uuid:f0cb86de-d6d3-4377-a33c-7c45bf6c9b71","An exploration to the impact of on-demand ride sharing services on urban tram systems","Wins, Jelle (TU Delft Civil Engineering and Geosciences)","Annema, Jan Anne (mentor); Milakis, Dimitris (mentor); van Wee, Bert (mentor); Delft University of Technology (degree granting institution)","2017","Traditional urban public transit may be on the verge of disruption with the global rise of new mobility services, such as on-demand ride-sharing, car-sharing and MaaS. Tram assets were found to be most vulnerable for disruption as the tram lacks the speed advantage of the metro and de bus has limited dedicated assets. Tram assets could run the risk of depreciation or disutility which can be a problem for the asset owner, tax payers and city residents. Digital On-demand Ride-sharing Services (DORS) are a likely suspect to disrupt the tram, because short term diffusion and substitution with transit is plausible according to literature. DORS providers include ride-sourcing, ride-sharing, ride-splitting, microtransit and combinations of them. All match supply and demand of available vehicles/seats using a digital platform. Scientific literature on potential impact of DORS on tram investments is lacking however. This thesis aimed to contribute to science by exploring the potential impact of DORS developments on investment policy in the tram system in an urban context. A framework for substitution was developed and four distinct scenarios for the impact of DORS on tram substitution and investments were written with the help of an expert panel. Moreover, the implications of these scenarios for tram investment policy were discussed. It was found that the development of DORS can have significant impact on tram investment policy, because substitution to some degree and risk of divestment is probable in three out of four scenarios. Disruptive impact however is probable in only one scenario and impact is larger for the already low demand tram corridors and less to non, for the bigger tram corridors. DORS can be beneficial for tram investments as well. To address this uncertainty in investment policy a robust and multiple strategy approach can be considered. MTA can also gamble by not considering the scenarios or choose one as truth, but this is not without risks.","Ride-sourcing; Ride-splitting; Disruptive innovations; Microtransit; Digital On-demand Ride Sharing Services (DORS); Tram; Investment policy; Scenario analysis","en","master thesis","","","","","","","","","","","","","",""