The rise of research into shared mobility systems reflects emerging challenges, such as rising traffic congestion, rising oil prices and rising environmental concern. The operations research community has turned towards more sharable and sustainable systems of transportation. Shared mobility systems can be collapsed into two main streams: Those where people share rides and those where parcel transportation and people transportation are combined. This survey sets out to review recent research in this area, including different optimization approaches, and to provide guidelines and promising directions for future research. It makes a distinction between prearranged and real-time problem settings and their methods of solution, and also gives an overview of real-case applications relevant to the research area.

https://hal.archives-ouvertes.fr/hal-02014342/document

A survey of models and algorithms for optimizing shared mobility

The introduction of autonomous (self-driving) and shared autonomous vehicles (AVs and SAVs) will affect travel destinations and distances, mode choice, and congestion. From a traffic perspective, although some congestion reduction may be achieved (thanks to fewer crashes and tighter headways), car-trip frequencies and vehicle miles traveled (VMT) are likely to rise significantly, reducing the benefits of driverless vehicles. Congestion pricing (CP) and road tolls are key tools for moderating demand and incentivizing more socially and environmentally optimal travel choices. This work develops multiple CP and tolling strategies in alternative future scenarios, and investigates their effects on the Austin, Texas network conditions and traveler welfare, using the agent-based simulation model MATSim. Results suggest that, while all pricing strategies reduce congestion, their social welfare impacts differ in meaningful ways. More complex and advanced strategies perform better in terms of traffic conditions and traveler welfare, depending on the development of the mobility landscape of autonomous driving. The possibility to refund users by reinvesting toll revenues as traveler budgets plays a salient role in the overall efficiency of each CP strategy as well as in the public acceptability.

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Congestion pricing in a world of self-driving vehicles: An analysis of different strategies in alternative future scenarios

Advancement in automated driving technology has created opportunities for smart urban mobility. Automated vehicles are now a popular topic with the rise of the smart city agenda. However, legislators, urban administrators, policymakers, and planners are unprepared to deal with the possible disruption of autonomous vehicles, which potentially could replace conventional transport. There is a lack of knowledge on how the new capabilities will disrupt and which policy strategies are needed to address such disruption. This paper aims to determine where we are, where we are headed, what the likely impacts of a wider uptake could be, and what needs to be done to generate desired smart urban mobility outcomes. The methodology includes a systematic review of the existing evidence base to understand capability, impact, planning, and policy issues associated with autonomous vehicles. The review reveals the trajectories of technological development, disruptive effects caused by such development, strategies to address the disruptions, and possible gaps in the literature. The paper develops a framework outlining the inter-links among driving forces, uptake factors, impacts and possible interventions. It concludes by advocating the necessity of preparing our cities for autonomous vehicles, although a wider uptake may take quite some time.

/pdf/understanding-autonomous-vehicles-a-systematic-literature-j2vdu3knz0.pdf

Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy

Many experts believe the transport system is about to change dramatically. This change is due to so-called fully-automated vehicles . However, at present, there are numerous important knowledge gaps that need to be solved for the successful integration of AVs in our transport systems, in particular regarding the impacts of AVs on travel demand. For instance, full automation will enable passengers to perform other, non-driving, related tasks while traveling to their destination. This may substantially change the way in which passengers experience traveling by car, and, in turn, may lead to considerable changes in the so-called Value of Travel Time . Many experts anticipate the VOTT to decrease substantially due to AVs. However, the extent to which VOTT will change is currently far from clear. This study aims to develop new insights on the potential impacts of fully automated vehicles on the VOTT for commute trips. To do so, we first look at the existing microeconomics theory on the perceived VOTT and analyze the expected changes accrued from the effect of working and having leisure in an AV. We conclude that the VOTT of a work vehicle should be lower than what is experienced today in a conventional vehicle but the leisure one could stay the same. Then we conduct a stated choice experiment, specifically designed and administered for measuring the VOTT, and analyze these data using discrete choice models . In total, we collected data from about 500 respondents. In the experiment, respondents were presented choice tasks consisting of three alternatives: an AV with office interior, an AV with leisure interior, and a conventional car. The same experiment was also given to another sample of respondents but this time describing a chauffeur-driven vehicle. Overall we find the average VOTT for an AV with an office interior to be lower than the VOTT for the conventional car , however the AV with leisure interior is not found to decrease the value of time which confirms the theoretical results. The VOTT for the chauffeur experiment is systematically lower than for the AV experiment which we attribute to some distrust that people have regarding the AVs. © 2018 Elsevier Ltd

/pdf/on-the-impact-of-vehicle-automation-on-the-value-of-travel-d7hi8hfkcp.pdf

On the impact of vehicle automation on the value of travel time while performing work and leisure activities in a car: Theoretical insights and results from a stated preference survey

Impact of ridesharing on operational efficiency of shared autonomous electric vehicle fleet

The last mile in a public transport trip is known to bring a large disutility for passengers, because the conventional transport modes for this stage of the trip can, in many cases, be rather slow, inflexible and not provide a seamless experience to passengers. Fully automated vehicles (AVs), that is, those which do not need a driver, could act as a first mile/last mile connection to mass public transport modes. In this paper, we study a system that we call Automated Last-Mile Transport (ALMT), which consists of a fleet of small, fully automated, electric vehicles to improve the last mile performance of a trip done in a train. An agent-based simulation model was proposed for the ALMT whereby a dispatching algorithm distributes travel requests amongst the available vehicles using a FIFO sequence and selects a vehicle based on a set of specified control conditions (e.g. travel time to reach a requesting passenger). The model was applied to the case-study of the connection between the train station Delft Zuid and the Technological Innovation Campus (Delft, The Netherlands) in order to test the methodology and understand the performance of the system in function of several operational parameters and demand scenarios. The most important conclusion from the baseline scenario was that the ALMT system was only able to compete with the walking mode and that additional measures were needed to increase the performance of the ALMT system in order to be competitive with cycling. Relocating empty vehicles or allowing pre-booking of vehicles led to a significant reduction in average waiting time, whilst allowing passengers to drive at a higher speed led to a large reduction in average travel time, whilst simultaneously reducing system capacity as energy use is increased.

http://pure.tudelft.nl/ws/files/28677169/1_s2.0_S2046043016300296_main.pdf

Exploring the use of automated vehicles as last mile connection of train trips through an agent-based simulation model: An application to Delft, Netherlands

De last-mile in een openbaar vervoer (OV)reis is een van de meest hinderlijke gedeelten van een reis perOV,hierdoor is het OV veelal niet in staat om te kunnen concurreren met de auto. De oorzaak kan deels worden gevonden in het gebrek aan flexibiliteit en de lage snelheden die de veelal conventionele vervoersmiddelen op de last-mile kenmerken.Recente technologische ontwikkelingen makeninnovatieve vraaggestuurde vervoersconceptenmet zelfrijdende voertuigen mogelijk. Deze zijn onafhankelijk van vaste infrastructuur en zouden ideaalop de last-mile ingezetkunnen worden, om zo het diffusepatroon en lage vervoersvolume efficient tebedienen. Om de vervoerwaarde en de prestatie van een vraaggestuurd vervoersysteem met zelfrijdende voertuigen op de last-mile te bepalen is alscase:Station Delft-Zuid –TU Delft gekozen.Bij een vervoerwaarde studie metdergelijke innovatieve techniek wordt verwachtdat psychologische factoren, naast depuur instrumentele aspecten, expliciet een rol spelen in de vervoerwijzekeuzevan reizigers. Het meenemen van deze preferenties is daarom van belang, teneindede vervoerwaarde niet substantieel te over-of onderschatten. In dit paper zijnde uitkomsten van twee onderzoeken gecombineerd, een instrumenteel onderzoek (simulatiemodel + enquete) naar de vervoerwaarde van een systeem van zelfrijdende voertuigen en een stated preference (SP)-experiment om de invloed van diversepsychologische factoren op de vervoerwaarde te bepalen.De resultaten laten zien dat er een aanzienlijke vervoerwaarde bestaat voor zelfrijdende voertuigen op de last-mile, te weten57% van de steekproefpopulatie.Methetsimulatiemodel zijn diverse ITS maatregelengesimuleerd, deze maatregelen omvatten wijzigingen in de netwerkstructuur, voertuiggedragen het reizigersgedrag.De prestatie van het last-mile systeem, bleekveelalpositiefbeinvloedt te worden door bovenstaande maatregelen.Effecten werden gemeten in een vergroting van de systeemcapaciteit, een reductie van de gemiddelde wachttijd ofwel van de gemiddelde reistijd. Het parallel toepassen van deze maatregelen realiseerteen aantrekkelijkere concurrentiepositie ten opzichte van de conventionele vervoerswijzen. De meest kenmerkende maatregel,het strategisch plaatsen van voertuigen,op locaties voorafgaand aan het ontstaan van vervoersvraag laat een reductie in de gemiddelde wachttijd van 40% zien.Uit het SP-experimentblijkt dat de attitudes ‘duurzaamheid’ en ‘vertrouwen’ de twee belangrijkste aspectenzijn in de vervoerwijzekeuze, terwijl de in-voertuigtijd niet minder negatief wordt ervaren dan in een door de reiziger gereden carsharing systeem. Dit suggereert dat de gebruikelijk genoemde voordelen van zelfrijdende voertuigenwellicht nog niet worden ervaren door de reiziger vanvandaag, en illustreert het belang om hier aandacht aan te schenken voor een succesvolle implementatievan zelfrijdende voertuigen.

Het verbeteren van de last-mile in een OV reis met automatische voertuigen : Een Delftse case studie en stated preference onderzoek gecombineerd

This paper gives an overview of the projects and pilots with automated vehicles as public transport in the Netherlands as well as the remaining research questions and preliminary results of passenger related studies regarding expected ridership.

AV meets PT: the future of automated vehicles in public transport

The continuing urbanization and corresponding increase in transport demand are putting pressure on the accessibility, safety, sustainability, livability, and efficiency of urbanized regions. Public transport is regarded as a sustainable mode of transport for these regions and therefore transport policies aim to increase its attractiveness. However, public transport is facing last-mile connectivity problems. The application of Autonomous Mobility on-Demand (AMoD) as a feeder service for public transport hubs can potentially improve the first and last-mile trip leg which increases the attractivity of public transport. However, will such a system be financially viable when applied in an urban area? and what kind of operation will lead to the highest system performance? In this research, this question is addressed by proposing a method that connects macro transport modeling and agent-based modeling (ABM). An existing gravity-based travel demand estimation model built in a macro simulation tool is used to predict passenger demand across all the OD pairs of a city. For those OD pairs that can use the AMoD as first /last mile this is modeled using an agent-based rationale to be able to simulate the behavior of passengers and vehicles within that specific area of the city. The simulation model is applied to the case study area of the south of Rotterdam, in The Netherlands, where metro Station Zuidplein and the rail Station Lombardijen function as two AMoD hubs. Using the case study, the impact of relocation, ridesharing, and charging strategy is assessed in regards to financial viability. Among other insights, results show that the AMoD service leads to a profit on a typical business day for the operating companies despite the high-quality level of the service (very low average waiting time for a vehicle). If this particular system would not consist of automated vehicles and one would have to pay a salary to drivers, it would not be possible to make a profit on a typical business day. Moreover, results show that activating dynamic ridesharing and using wireless fast chargers at the stations results in the most financially viable operation. Activating automatic relocations results in the most costly operation. 

https://repository.tudelft.nl/islandora/object/uuid%3A82dfb02e-403b-4526-bf16-e4c11e501054/datastream/OBJ/download

An agent-based model for assessing the financial viability of autonomous mobility on-demand systems used as first and last-mile of public transport trips: A case-study in Rotterdam, the Netherlands

The last mile in a public transport trip appears to be one of the main deterrents in public transport to be competitive with other modes of transport. The reason for this bad performance of the last mile can be related to the slow and inflexible character of the last mile. Where PRT systems aspire to deliver an on demand, direct service to a passenger, they are bound to fixed separated infrastructure and would therefore face high investment costs for the application of PRT systems on the last mile. As automated vehicles can make use of any kind of road that is available the investment costs are considerably lower. The system presented in this thesis is a last mile transportation service operated by automated vehicles on existing infrastructure. Multiple operational strategies have been simulated using an agent based simulation model in AnyLogic. This simulation model has been applied on the case study Delft Zuid - Technological Innovation campus. For this case study a travel demand survey has been conducted, from which the results serve as one of the main inputs of the simulation model. The outcomes of the simulations indicate that relocating empty vehicles, intermediate charging have a positive effect on the performance of the system on the last mile without compromising any other system performance parameter. Pre-booking of vehicles (via a smartphone app) showed to be very beneficial with regard to the average waiting time for a passenger. However, as vehicles are locked for a longer period the system capacity decreases. The speed of the vehicles appeared one of the main determinants for the energy use of the vehicles and therefore the available system capacity. Speed variations have shown large reductions in the average travel time for a passenger.

Arthur Scheltes

Papers

Exploring the use of automated vehicles as last mile connection of train trips through an agent-based simulation model: An application to Delft, Netherlands

Het verbeteren van de last-mile in een OV reis met automatische voertuigen : Een Delftse case studie en stated preference onderzoek gecombineerd

AV meets PT: the future of automated vehicles in public transport

An agent-based model for assessing the financial viability of autonomous mobility on-demand systems used as first and last-mile of public transport trips: A case-study in Rotterdam, the Netherlands

Improving the last mile in a public transport trip with automated vehicles using an agent based simulation model: A Delft case study