From 2ffb33f84db8e1cefc29c6c9077652c30e4fdd94 Mon Sep 17 00:00:00 2001 From: Felix Jung Date: Mon, 4 Dec 2023 19:09:20 +0100 Subject: [PATCH] added joss manuscript --- .github/workflows/joss-draft-pdf.yml | 22 ++ joss/paper.bib | 502 +++++++++++++++++++++++++++ joss/paper.md | 72 ++++ 3 files changed, 596 insertions(+) create mode 100644 .github/workflows/joss-draft-pdf.yml create mode 100644 joss/paper.bib create mode 100644 joss/paper.md diff --git a/.github/workflows/joss-draft-pdf.yml b/.github/workflows/joss-draft-pdf.yml new file mode 100644 index 00000000..ebeb836f --- /dev/null +++ b/.github/workflows/joss-draft-pdf.yml @@ -0,0 +1,22 @@ +name: JOSS draft + +on: + - push + +jobs: + paper: + runs-on: ubuntu-latest + name: Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + paper-path: joss/paper.md + - name: Upload + uses: actions/upload-artifact@v3 + with: + name: paper + path: joss/paper.pdf diff --git a/joss/paper.bib b/joss/paper.bib new file mode 100644 index 00000000..73e2cf72 --- /dev/null +++ b/joss/paper.bib @@ -0,0 +1,502 @@ +@book{behrisch2014, + title = {Simulation of {{Urban Mobility}}: {{First International Conference}}, {{SUMO}} 2013, {{Berlin}}, {{Germany}}, {{May}} 15-17, 2013. {{Revised Selected Papers}}}, + shorttitle = {Simulation of {{Urban Mobility}}}, + editor = {Behrisch, Michael and Krajzewicz, Daniel and Weber, Melanie}, + year = {2014}, + series = {Information {{Systems}} and {{Applications}}, Incl. {{Internet}}/{{Web}}, and {{HCI}}}, + edition = {1st ed. 2014}, + number = {8594}, + publisher = {{Springer Berlin Heidelberg : Imprint: Springer}}, + address = {{Berlin, Heidelberg}}, + doi = {10.1007/978-3-662-45079-6}, + abstract = {This book constitutes the thoroughly refereed proceedings of the First International Conference on Simulation of Urban Mobility, SUMO 2013, held in Berlin, Germany, in May 2013. The 12 revised full papers presented tin this book were carefully selected and reviewed from 22 submissions. The papers are organized in two topical sections: models and technical innovations and applications and surveys}, + isbn = {978-3-662-45079-6}, + lccn = {003.3}, + keywords = {Application software,Computer Applications,Computer simulation,Simulation and Modeling}, + file = {/home/jung/Zotero/storage/J4B375AA/Behrisch et al. - 2014 - Simulation of Urban Mobility First International .pdf} +} + +@article{bezanson2017, + title = {Julia: {{A Fresh Approach}} to {{Numerical Computing}}}, + shorttitle = {Julia}, + author = {Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and Shah, Viral B.}, + year = {2017}, + month = jan, + journal = {SIAM Rev.}, + volume = {59}, + number = {1}, + pages = {65--98}, + issn = {0036-1445, 1095-7200}, + doi = {10.1137/141000671}, + urldate = {2023-04-25}, + langid = {english}, + file = {/home/jung/Zotero/storage/SRV952D3/Bezanson et al. - 2017 - Julia A Fresh Approach to Numerical Computing.pdf} +} + +@article{cython, + ids = {behnel2011}, + title = {Cython: {{The Best}} of {{Both Worlds}}}, + shorttitle = {Cython}, + author = {Behnel, Stefan and Bradshaw, Robert and Citro, Craig and Dalcin, Lisandro and Seljebotn, Dag Sverre and Smith, Kurt}, + year = {2011}, + month = mar, + journal = {Comput. Sci. Eng.}, + volume = {13}, + number = {2}, + pages = {31--39}, + issn = {1521-9615}, + doi = {10.1109/MCSE.2010.118}, + urldate = {2023-04-24} +} + +@article{deruijter2023, + title = {Ride-Pooling Adoption, Efficiency and Level of Service under Alternative Demand, Behavioural and Pricing Settings}, + author = {{de Ruijter}, Arjan and Cats, Oded and {Alonso-Mora}, Javier and Hoogendoorn, Serge}, + year = {2023}, + month = may, + journal = {Transp. Plan. Technol.}, + volume = {46}, + number = {4}, + pages = {407--436}, + publisher = {{Routledge}}, + issn = {0308-1060}, + doi = {10.1080/03081060.2023.2194874}, + urldate = {2023-12-02}, + abstract = {Previous studies into the potential benefits of ride pooling failed to account for the trade-off that users likely make when considering a shared ride. We address this shortcoming by formulating user net benefit stemming from pooling as a compensatory function where the additional travel time and on-board discomfort need to be compensated by the price discount for a traveller to choose a pooled ride over a private ride. The proposed formulation is embedded in a method for matching travel requests and vehicles. We conduct a series of experiments investigating how the potential of ride-pooling services depends on demand characteristics, user preferences and the pricing policy adopted by the service provider. Our results suggest that the total vehicle mileage savings found by previous studies is only attainable when users are very willing to share their ride (i.e. attach low premium to private rides) and are offered a 50\% discount for doing so.}, + keywords = {delay tolerance,demand distribution,pricing,Ride-pooling,willingness to share} +} + +@inproceedings{engelhardt2019, + ids = {engelhardt2019a}, + title = {Quantifying the {{Benefits}} of {{Autonomous On-Demand Ride-Pooling}}: {{A Simulation Study}} for {{Munich}}, {{Germany}}}, + shorttitle = {Quantifying the {{Benefits}} of {{Autonomous On-Demand Ride-Pooling}}}, + booktitle = {2019 {{IEEE Intell}}. {{Transp}}. {{Syst}}. {{Conf}}. {{ITSC}}}, + author = {Engelhardt, Roman and Dandl, Florian and Bilali, Aledia and Bogenberger, Klaus}, + year = {2019}, + month = oct, + pages = {2992--2997}, + doi = {10.1109/ITSC.2019.8916955}, + abstract = {Autonomous on-demand mobility systems, especially ride-pooling services, except for providing convenient transportation for the people, could potentially improve the traffic congestion in urban environments by reducing the number of private vehicles. In this paper, we introduce an Autonomous On-Demand Ride-Pooling (AODRP) system, which uses a rather realistic customer-model that is sensitive to waiting times. To quantify the benefits that the AODRP system could have on a city network, a case study in Munich is performed with a shared fleet of vehicles. Different scenarios, in which private vehicle trips are partly replaced with ride-pooling trips until an adoption rate of 15\%, are investigated for varying allowed customer detour times. The results show that the benefits of an AODRP service are observed from a certain adoption rate. For low demand level of 1\%, the ride-pooling service even increases Vehicle Miles Traveled (VMT) in the system, due to the empty trips generated while going to pick up customers. For higher adoption rates, pooling makes up for the additional empty VMT starting from approximately 5\% adoption rate. An analysis of change in VMT per road type reveals that the AODRP system especially reduces traffic on major roads, in which nowadays the highest level of congestion is observed, while extra VMT due to empty pick-up trips are concentrated on minor roads.}, + keywords = {Business,Conferences,Linear programming,Pollution measurement,Roads,Urban areas}, + file = {/home/jung/Zotero/storage/8SVLQS3B/Engelhardt et al. - 2019 - Quantifying the Benefits of Autonomous On-Demand R.pdf;/home/jung/Zotero/storage/U9LZY55J/8916955.html} +} + +@misc{engelhardt2022, + title = {{{FleetPy}}: {{A Modular Open-Source Simulation Tool}} for {{Mobility On-Demand Services}}}, + shorttitle = {{{FleetPy}}}, + author = {Engelhardt, Roman and Dandl, Florian and Syed, Arslan-Ali and Zhang, Yunfei and Fehn, Fabian and Wolf, Fynn and Bogenberger, Klaus}, + year = {2022}, + month = jul, + number = {arXiv:2207.14246}, + eprint = {2207.14246}, + primaryclass = {cs, eess}, + publisher = {{arXiv}}, + doi = {10.48550/arXiv.2207.14246}, + urldate = {2023-04-25}, + abstract = {The market share of mobility on-demand (MoD) services strongly increased in recent years and is expected to rise even higher once vehicle automation is fully available. These services might reduce space consumption in cities as fewer parking spaces are required if private vehicle trips are replaced. If rides are shared additionally, occupancy related traffic efficiency is increased. Simulations help to identify the actual impact of MoD on a traffic system, evaluate new control algorithms for improved service efficiency and develop guidelines for regulatory measures. This paper presents the open-source agent-based simulation framework FleetPy. FleetPy (written in the programming language "Python") is explicitly developed to model MoD services in a high level of detail. It specially focuses on the modeling of interactions of users with operators while its flexibility allows the integration and embedding of multiple operators in the overall transportation system. Its modular structure ensures the transferabillity of previously developed elements and the selection of an appropriate level of modeling detail. This paper compares existing simulation frameworks for MoD services and highlights exclusive features of FleetPy. The upper level simulation flows are presented, followed by required input data for the simulation and the output data FleetPy produces. Additionally, the modules within FleetPy and high-level descriptions of current implementations are provided. Finally, an example showcase for Manhattan, NYC provides insights into the impacts of different modules for simulation flow, fleet optimization, traveler behavior and network representation.}, + archiveprefix = {arxiv}, + keywords = {Computer Science - Multiagent Systems,Electrical Engineering and Systems Science - Systems and Control}, + file = {/home/jung/Zotero/storage/TDB8Q2Y8/Engelhardt et al. - 2022 - FleetPy A Modular Open-Source Simulation Tool for.pdf;/home/jung/Zotero/storage/A39WZKPX/2207.html} +} + +@article{henao2019a, + title = {The {{Impact}} of {{Ride-Hailing}} on {{Vehicle Miles Traveled}}}, + author = {Henao, Alejandro and Marshall, Wesley E.}, + year = {2019}, + month = dec, + journal = {Transportation}, + volume = {46}, + number = {6}, + pages = {2173--2194}, + issn = {1572-9435}, + doi = {10.1007/s11116-018-9923-2}, + urldate = {2022-04-26}, + abstract = {Ride-haling such as Uber and Lyft are changing the ways people travel. Despite widespread claims that these services help reduce driving, there is little research on this topic. This research paper uses a quasi-natural experiment in the Denver, Colorado, region to analyze basic impacts of ride-hailing on transportation efficiency in terms of deadheading, vehicle occupancy, mode replacement, and vehicle miles traveled (VMT). Realizing the difficulty in obtaining data directly from Uber and Lyft, we designed a quasi-natural experiment{\textemdash}by one of the authors driving for both companies{\textemdash}to collect primary data. This experiment uses an ethnographic and survey-based approach that allows the authors to gain access to exclusive data and real-time passenger feedback. The dataset includes actual travel attributes from 416 ride-hailing rides{\textemdash}Lyft, UberX, LyftLine, and UberPool{\textemdash}and travel behavior and socio-demographics from 311 passenger surveys. For this study, the conservative (lower end) percentage of deadheading miles from ride-hailing is 40.8\%. The average vehicle occupancy is 1.4 passengers per ride, while the distance weighted vehicle occupancy is 1.3 without accounting for deadheading and 0.8 when accounting deadheading. When accounting for mode replacement and issues such as driver deadheading, we estimate that ride-hailing leads to approximately 83.5\% more VMT than would have been driven had ride-hailing not existed. Although our data collection focused on the Denver region, these results provide insight into the impacts of ride-hailing.}, + keywords = {Deadheading,Lyft,Mode replacement,Ride-hailing,Ridesourcing,TNC,Uber,Vehicle miles traveled,Vehicle occupancy,VMT}, + file = {/home/jung/Zotero/storage/JMXTR2XK/Henao and Marshall - 2019 - The impact of ride-hailing on vehicle miles travel.pdf} +} + +@article{herminghaus2019a, + title = {Mean {{Field Theory}} of {{Demand Responsive Ride Pooling Systems}}}, + author = {Herminghaus, Stephan}, + year = {2019}, + journal = {Transp Res Policy Pr.}, + volume = {119}, + pages = {15--28}, + file = {/home/jung/Zotero/storage/MVQ7HVM9/Herminghaus - 2019 - Mean field theory of demand responsive ride poolin.pdf;/home/jung/Zotero/storage/239T62BM/S0965856417316038.html} +} + +@book{horni2016, + title = {The {{Multi-Agent Transport Simulation MATSim}}}, + editor = {{ETH Z{\"u}rich} and Horni, Andreas and Nagel, Kai and {TU Berlin} and Axhausen, Kay W.}, + year = {2016}, + month = aug, + publisher = {{Ubiquity Press}}, + url = {http://www.ubiquitypress.com/site/books/10.5334/baw/}, + urldate = {2023-04-25}, + isbn = {978-1-909188-75-4}, + keywords = {Agent-based modeling,Choice modeling,Large-scale simulation,Transport modeling,Transport planning}, + file = {/home/jung/Zotero/storage/AEN4AYT7/Axhausen and ETH Zürich - 2016 - The Multi-Agent Transport Simulation MATSim.pdf} +} + +@misc{json, + title = {The {{JavaScript Object Notation}} ({{JSON}}) {{Data Interchange Format}}}, + author = {Bray, T.}, + year = {2017}, + month = dec, + url = {https://www.rfc-editor.org/rfc/rfc4180.txt}, + langid = {english} +} + +@article{kucharski2022a, + ids = {kucharski2022b}, + title = {Simulating Two-Sided Mobility Platforms with {{MaaSSim}}}, + author = {Kucharski, Rafa{\l} and Cats, Oded}, + year = {2022}, + month = jun, + journal = {PLOS ONE}, + volume = {17}, + number = {6}, + pages = {e0269682}, + publisher = {{Public Library of Science}}, + issn = {1932-6203}, + doi = {10.1371/journal.pone.0269682}, + urldate = {2023-04-06}, + abstract = {Two-sided mobility platforms, such as Uber and Lyft, widely emerged in the urban mobility landscape. Distributed supply of individual drivers, matched with travellers via intermediate platform yields a new class of phenomena not present in urban mobility before. Such disruptive changes to transportation systems call for a simulation framework where researchers from various and across disciplines may introduce models aimed at representing the complex dynamics of platform-driven urban mobility. In this work, we present MaaSSim, a lightweight agent-based simulator reproducing the transport system used by two kinds of agents: (i) travellers, requesting to travel from their origin to destination at a given time, and (ii) drivers supplying their travel needs by offering them rides. An intermediate agent, the platform, matches demand with supply. Agents are individual decision-makers. Specifically, travellers may decide which mode they use or reject an incoming offer; drivers may opt-out from the system or reject incoming requests. All of the above behaviours are modelled through user-defined modules, allowing to represent agents' taste variations (heterogeneity), their previous experiences (learning) and available information (system control). MaaSSim is a flexible open-source python library capable of realistically reproducing complex interactions between agents of a two-sided mobility platform. MaaSSim is available from a public repository, along with a set of tutorials and reproducible use-case scenarios, as demonstrated with a series of illustrative examples and a comprehensive case study.}, + langid = {english}, + keywords = {Agent-based modeling,Decision making,Human mobility,Learning,Roads,Salaries,Supply and demand,Transportation}, + file = {/home/jung/Zotero/storage/YNJM2RPY/Kucharski and Cats - 2022 - Simulating two-sided mobility platforms with MaaSS.pdf} +} + +@inproceedings{lopez2018, + title = {Microscopic {{Traffic Simulation}} Using {{SUMO}}}, + booktitle = {2018 21st {{Int}}. {{Conf}}. {{Intell}}. {{Transp}}. {{Syst}}. {{ITSC}}}, + author = {Lopez, Pablo Alvarez and Behrisch, Michael and {Bieker-Walz}, Laura and Erdmann, Jakob and Fl{\"o}tter{\"o}d, Yun-Pang and Hilbrich, Robert and L{\"u}cken, Leonhard and Rummel, Johannes and Wagner, Peter and Wiessner, Evamarie}, + year = {2018}, + month = nov, + pages = {2575--2582}, + issn = {2153-0017}, + doi = {10.1109/ITSC.2018.8569938}, + abstract = {Microscopic traffic simulation is an invaluable tool for traffic research. In recent years, both the scope of research and the capabilities of the tools have been extended considerably. This article presents the latest developments concerning intermodal traffic solutions, simulator coupling and model development and validation on the example of the open source traffic simulator SUMO.}, + keywords = {Data models,Mathematical model,Microscopy,Roads,Tools,Urban areas,Vehicle dynamics}, + file = {/home/jung/Zotero/storage/SRJPJLYR/Lopez et al. - 2018 - Microscopic Traffic Simulation using SUMO.pdf;/home/jung/Zotero/storage/8G86T55T/8569938.html} +} + +@article{lotze2022b, + title = {Dynamic Stop Pooling for Flexible and Sustainable Ride Sharing}, + author = {Lotze, Charlotte and Marszal, Philip and Schr{\"o}der, Malte and Timme, Marc}, + year = {2022}, + month = mar, + journal = {New J. Phys.}, + volume = {24}, + number = {2}, + pages = {023034}, + publisher = {{IOP Publishing}}, + issn = {1367-2630}, + doi = {10.1088/1367-2630/ac47c9}, + urldate = {2023-12-02}, + abstract = {Ride sharing{\textemdash}the bundling of simultaneous trips of several people in one vehicle{\textemdash}may help to reduce the carbon footprint of human mobility. However, the complex collective dynamics pose a challenge when predicting the efficiency and sustainability of ride sharing systems. Standard door-to-door ride sharing services trade reduced route length for increased user travel times and come with the burden of many stops and detours to pick up individual users. Requiring some users to walk to nearby shared stops reduces detours, but could become inefficient if spatio-temporal demand patterns do not well fit the stop locations. Here, we present a simple model of dynamic stop pooling with flexible stop positions. We analyze the performance of ride sharing services with and without stop pooling by numerically and analytically evaluating the steady state dynamics of the vehicles and requests of the ride sharing service. Dynamic stop pooling does a priori not save route length, but occupancy. Intriguingly, it also reduces the travel time, although users walk parts of their trip. Together, these insights explain how dynamic stop pooling may break the trade-off between route lengths and travel time in door-to-door ride sharing, thus enabling higher sustainability and service quality. Video Abstract: Dynamic stop pooling for flexible and sustainable ride sharing}, + langid = {english}, + file = {/home/jung/Zotero/storage/GWF462Q3/Lotze et al. - 2022 - Dynamic stop pooling for flexible and sustainable .pdf} +} + +@misc{lyft, + title = {Lyft}, + author = {Lyft, {\relax Inc}.}, + year = {2023}, + url = {https://www.lyft.com/}, + urldate = {2023-04-25} +} + +@article{manik2020a, + title = {Topology Dependence of On-Demand Ride-Sharing}, + author = {Manik, Debsankha and Molkenthin, Nora}, + year = {2020}, + month = aug, + journal = {Appl Netw Sci}, + volume = {5}, + number = {1}, + pages = {49}, + issn = {2364-8228}, + doi = {10.1007/s41109-020-00290-2}, + urldate = {2023-12-02}, + abstract = {Traffic is a challenge in rural and urban areas alike with negative effects ranging from congestion to air pollution. Ride-sharing poses an appealing alternative to personal cars, combining the traffic-reducing ride bundling of public transport with much of the flexibility and comfort of personal cars. Here we study the effects of the underlying street network topology on the viability of ride bundling analytically and in simulations. Using numerical and analytical approaches we find that system performance can be measured in the number of scheduled stops per vehicle. Its scaling with the request rate is approximately linear and the slope, that depends on the network topology, is a measure of the ease of ridesharing in that topology. This dependence is caused by the different growth of the route volume, which we compute analytically for the simplest networks served by a single vehicle.}, + langid = {english}, + file = {/home/jung/Zotero/storage/9SNGH39H/Manik and Molkenthin - 2020 - Topology dependence of on-demand ride-sharing.pdf} +} + +@article{molkenthin2020, + title = {Scaling {{Laws}} of {{Collective Ride-Sharing Dynamics}}}, + author = {Molkenthin, Nora and Schr{\"o}der, Malte and Timme, Marc}, + year = {2020}, + month = dec, + journal = {Phys. Rev. Lett.}, + volume = {125}, + number = {24}, + pages = {248302}, + publisher = {{American Physical Society}}, + doi = {10.1103/PhysRevLett.125.248302}, + urldate = {2022-08-31}, + abstract = {Ride-sharing services may substantially contribute to future sustainable mobility. Their collective dynamics intricately depend on the topology of the underlying street network, the spatiotemporal demand distribution, and the dispatching algorithm. The efficiency of ride-sharing fleets is thus hard to quantify and compare in a unified way. Here, we derive an efficiency observable from the collective nonlinear dynamics and show that it exhibits a universal scaling law. For any given dispatcher, we find a common scaling that yields data collapse across qualitatively different topologies of model networks and empirical street networks from cities, islands, and rural areas. A mean-field analysis confirms this view and reveals a single scaling parameter that jointly captures the influence of network topology and demand distribution. These results further our conceptual understanding of the collective dynamics of ride-sharing fleets and support the evaluation of ride-sharing services and their transfer to previously unserviced regions or unprecedented demand patterns.}, + file = {/home/jung/Zotero/storage/A2GDNDIG/Molkenthin et al. - 2020 - Scaling Laws of Collective Ride-Sharing Dynamics.pdf;/home/jung/Zotero/storage/BMY6D6F8/PhysRevLett.125.html} +} + +@misc{muehle2022, + title = {{{RidePoolingSimulations}}}, + author = {M{\"u}hle, Steffen}, + year = {2022}, + month = jun, + url = {https://github.com/SteffenMuehle/RidePoolingSimulations}, + urldate = {2023-04-25}, + abstract = {Simulation code, animations and results from the paper "A framework for modeling ride pooling efficiency and minimum fleet size"}, + copyright = {MIT} +} + +@article{muehle2023, + ids = {muhle2023}, + title = {An Analytical Framework for Modeling Ride Pooling Efficiency and Minimum Fleet Size}, + author = {M{\"u}hle, Steffen}, + year = {2023}, + month = jun, + journal = {Multimodal Transportation}, + volume = {2}, + number = {2}, + pages = {100080}, + issn = {2772-5863}, + doi = {10.1016/j.multra.2023.100080}, + urldate = {2023-03-02}, + abstract = {Ride pooling (RP) is a transport mode using on-demand buses to combine the trips of multiple users into one vehicle. Its required fleet size and carbon emissions are quantified by the system's efficiency. Due to the complex interplay between street network, buses, users and dispatch algorithm, efficiency case studies are available but bottom-up predictions are not. Here we close this gap using probabilistic and geometric arguments in an analytical model framework. Its modular design allows for adaptation to specific usage scenarios and provides an over-arching view of them. In a showcase on Euclidean spaces, our model quantifies how RP outperforms private cars as user demand increases. Its predicted power-law scaling is verified using a custom simulation framework, which further reveals improved scaling on real street networks and graphs with hierarchical structures. Henceforth, our work may help to identify street networks well-suited for RP, and predict key performance indicators analytically.}, + langid = {english}, + keywords = {Demand-responsive transport,Mobility as a service,Ride pooling,Ride sharing,Street network,Sustainability}, + file = {/home/jung/Zotero/storage/4MHIX7EG/Mühle - 2023 - An analytical framework for modeling ride pooling .pdf;/home/jung/Zotero/storage/FITLHAI4/S2772586323000126.html} +} + +@misc{pytest, + title = {Pytest}, + author = {Krekel, Holger and Oliveira, Bruno and Pfannschmidt, Ronny and Bruynooghe, Floris and Laugher, Brianna and Bruhin, Florian}, + year = {2004}, + url = {https://github.com/pytest-dev/pytest} +} + +@misc{ridepy-doc, + title = {{{RidePy}} Documentation}, + author = {{Felix Jung} and {Debsankha Manik}}, + year = {2023}, + url = {https://ridepy.org/}, + urldate = {2023-04-25}, + file = {/home/jung/Zotero/storage/YWE3DN2M/ridepy.org.html} +} + +@misc{ridepy-github, + title = {{{PhysicsOfMobility}}/Ridepy - Github}, + author = {{Felix Jung} and {Debsankha Manik}}, + year = {2020}, + url = {https://github.com/PhysicsOfMobility/ridepy}, + urldate = {2023-04-25}, + abstract = {Simulates a dispatching algorithm serving exogenous transportation requests with a fleet of vehicles. Does not simulate the universe, unlike MATSim. Batteries are included.}, + copyright = {MIT}, + keywords = {research,ridepooling,simulation} +} + +@misc{ridepy-pypi, + title = {Ridepy - {{PyPI}}}, + author = {{Felix Jung} and {Debsankha Manik}}, + year = {2023}, + url = {https://pypi.org/project/ridepy/}, + urldate = {2023-04-25}, + copyright = {MIT License}, + keywords = {{mobility,},physics,{ridepooling,},Scientific/Engineering - Physics,{simulation,},{transport,}}, + file = {/home/jung/Zotero/storage/JAZNH2VG/ridepy.html} +} + +@inproceedings{ruch2018, + title = {{{AMoDeus}}, a {{Simulation-Based Testbed}} for {{Autonomous Mobility-on-Demand Systems}}}, + booktitle = {2018 21st {{Int}}. {{Conf}}. {{Intell}}. {{Transp}}. {{Syst}}. {{ITSC}}}, + author = {Ruch, Claudio and Horl, Sebastian and Frazzoli, Emilio}, + year = {2018}, + month = nov, + pages = {3639--3644}, + publisher = {{IEEE}}, + address = {{Maui, HI}}, + doi = {10.1109/ITSC.2018.8569961}, + urldate = {2023-04-25}, + isbn = {978-1-72810-321-1 978-1-72810-323-5} +} + +@article{ruch2020, + title = {Quantifying the {{Efficiency}} of {{Ride Sharing}}}, + author = {Ruch, Claudio and Lu, ChengQi and Sieber, Lukas and Frazzoli, Emilio}, + year = {2020}, + journal = {IEEE Trans. Intell. Transp. Syst.}, + pages = {1--6}, + issn = {1558-0016}, + doi = {10.1109/TITS.2020.2990202}, + abstract = {In unit-capacity mobility-on-demand systems, the vehicles transport only one travel party at a time, whereas in ride-sharing mobility-on-demand systems, a vehicle may transport different travel parties at the same time, e.g., if paths are partially overlapping. One potential benefit of ride sharing is increased system efficiency. However, it is not clear what the trade-offs are between the efficiency gains and the reduction in quality of service. To quantify those trade-offs, an open-source simulation environment is introduced, which is capable of evaluating a large class of operational policies for ride-sharing mobility-on-demand systems. The impact of ride sharing on efficiency and service level is assessed for several benchmark operational policies from the literature and for different transportation scenarios: first a dense urban scenario, then a line-shaped, rural one. Based on the results of these case studies, we find that the efficiency gains in ride sharing are relatively small and potentially hard to justify against quality of service concerns such as reduced convenience, loss of privacy, and higher total travel and drive times. Furthermore, in the assessed scenarios, the relatively low occupancy of the vehicles suggests that smaller vehicles with 4-6 seats, able to handle occasional ride sharing, may be preferable to larger and more expensive vehicles such as minibuses.}, + keywords = {Benchmark testing,mobility on demand,operational policies.,Public transportation,Quality of service,Ride sharing,Urban areas,Vehicle dynamics}, + file = {/home/jung/Zotero/storage/HN3WG9PC/Ruch et al. - 2020 - Quantifying the Efficiency of Ride Sharing.pdf;/home/jung/Zotero/storage/5LHCMD8Y/9089254.html;/home/jung/Zotero/storage/RX6FX6Q9/9089254.html} +} + +@article{santi2014, + ids = {santi2014a}, + title = {Quantifying the Benefits of Vehicle Pooling with Shareability Networks}, + author = {Santi, Paolo and Resta, Giovanni and Szell, Michael and Sobolevsky, Stanislav and Strogatz, Steven H. and Ratti, Carlo}, + year = {2014}, + month = sep, + journal = {PNAS}, + volume = {111}, + number = {37}, + pages = {13290--13294}, + publisher = {{National Academy of Sciences}}, + issn = {0027-8424, 1091-6490}, + doi = {10.1073/pnas.1403657111}, + urldate = {2021-08-26}, + abstract = {Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network, which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40\% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting toward a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.}, + chapter = {Physical Sciences}, + copyright = {{\textcopyright} . Freely available online through the PNAS open access option.}, + langid = {english}, + pmid = {25197046}, + keywords = {carpooling,human mobility,maximum matching,urban computing}, + file = {/home/jung/Zotero/storage/WT6Z5ETZ/Santi et al. - 2014 - Quantifying the benefits of vehicle pooling with s.pdf;/home/jung/Zotero/storage/ZM7ZKQ49/13290.html} +} + +@book{stroustrup2000, + title = {The {{C}}++ Programming Language}, + author = {Stroustrup, Bjarne}, + year = {2000}, + publisher = {{Pearson Education India}} +} + +@article{tachet2017a, + title = {Scaling {{Law}} of {{Urban Ride Sharing}}}, + author = {Tachet, R. and Sagarra, O. and Santi, P. and Resta, G. and Szell, M. and Strogatz, S. H. and Ratti, C.}, + year = {2017}, + month = mar, + journal = {Sci Rep}, + volume = {7}, + number = {1}, + pages = {42868}, + publisher = {{Nature Publishing Group}}, + issn = {2045-2322}, + doi = {10.1038/srep42868}, + urldate = {2021-08-26}, + keywords = {Energy and society,Environmental social sciences}, + file = {/home/jung/Zotero/storage/5HSG5V26/Tachet et al. - 2017 - Scaling Law of Urban Ride Sharing.pdf;/home/jung/Zotero/storage/6GUEL3EB/Tachet et al. - 2017 - Scaling Law of Urban Ride Sharing.pdf;/home/jung/Zotero/storage/JNGDT5VS/Tachet et al. - 2017 - Scaling Law of Urban Ride Sharing.pdf;/home/jung/Zotero/storage/HFYIVQ5D/srep42868.html;/home/jung/Zotero/storage/XED7A83S/srep42868.html} +} + +@misc{uber, + title = {Uber}, + author = {Uber Technologies, {\relax Inc}.}, + year = {2023}, + url = {https://www.uber.com/}, + urldate = {2023-04-25} +} + +@book{vanrossum1995, + title = {Python Reference Manual}, + author = {Van Rossum, Guido and Drake Jr, Fred L}, + year = {1995}, + publisher = {{Centrum voor Wiskunde en Informatica Amsterdam}} +} + +@inproceedings{wesmckinney2010, + title = {Data {{Structures}} for {{Statistical Computing}} in {{Python}}}, + booktitle = {Proc. 9th {{Python Sci}}. {{Conf}}.}, + author = {{Wes McKinney}}, + editor = {{van der Walt}, St{\'e}fan and {Jarrod Millman}}, + year = {2010}, + pages = {56--61}, + doi = {10.25080/Majora-92bf1922-00a} +} + +@article{wilkes2021a, + title = {Self-{{Regulating Demand}} and {{Supply Equilibrium}} in {{Joint Simulation}} of {{Travel Demand}} and a {{Ride-Pooling Service}}}, + author = {Wilkes, Gabriel and Engelhardt, Roman and Briem, Lars and Dandl, Florian and Vortisch, Peter and Bogenberger, Klaus and Kagerbauer, Martin}, + year = {2021}, + month = aug, + journal = {Transp. Res. Rec.}, + volume = {2675}, + number = {8}, + pages = {226--239}, + publisher = {{SAGE Publications Inc}}, + issn = {0361-1981}, + doi = {10.1177/0361198121997140}, + urldate = {2023-12-02}, + abstract = {This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0{\textendash}150 vehicles), fares, and further fleet operators' settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6\% and 16.8\% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.}, + langid = {english}, + file = {/home/jung/Zotero/storage/UQQ35L7D/Wilkes et al. - 2021 - Self-Regulating Demand and Supply Equilibrium in J.pdf} +} + +@article{winkler2023, + title = {The Effect of Sustainable Mobility Transition Policies on Cumulative Urban Transport Emissions and Energy Demand}, + author = {Winkler, Lisa and Pearce, Drew and Nelson, Jenny and Babacan, Oytun}, + year = {2023}, + month = apr, + journal = {Nat Commun}, + volume = {14}, + number = {1}, + pages = {2357}, + publisher = {{Nature Publishing Group}}, + issn = {2041-1723}, + doi = {10.1038/s41467-023-37728-x}, + urldate = {2023-04-25}, + abstract = {The growing urban transport sector presents towns and cities with an escalating challenge in the reduction of their greenhouse gas emissions. Here we assess the effectiveness of several widely considered policy options (electrification, light-weighting, retrofitting, scrapping, regulated manufacturing standards and modal shift) in achieving the transition to sustainable urban mobility in terms of their emissions and energy impact until 2050. Our analysis investigates the severity of actions needed to comply with Paris compliant regional sub-sectoral carbon budgets. We introduce the Urban Transport Policy Model (UTPM) for passenger car fleets and use London as an urban case study to show that current policies are insufficient to meet climate targets. We conclude that, as well as implementation of emission-reducing changes in vehicle design, a rapid and large-scale reduction in car use is necessary to meet stringent carbon budgets and avoid high energy demand. Yet, without increased consensus in sub-national and sectoral carbon budgets, the scale of reduction necessary stays uncertain. Nevertheless, it is certain we need to act urgently and intensively across all policy mechanisms available as well as developing new policy options.}, + copyright = {2023 The Author(s)}, + langid = {english}, + keywords = {Energy and behaviour,Energy infrastructure,Energy modelling}, + file = {/home/jung/Zotero/storage/7UTS54ZX/Winkler et al. - 2023 - The effect of sustainable mobility transition poli.pdf} +} + +@article{zech2022, + ids = {zech2022a}, + title = {Collective Dynamics of Capacity-Constrained Ride-Pooling Fleets}, + author = {Zech, Robin M. and Molkenthin, Nora and Timme, Marc and Schr{\"o}der, Malte}, + year = {2022}, + month = jun, + journal = {Sci Rep}, + volume = {12}, + number = {1}, + pages = {10880}, + publisher = {{Nature Publishing Group}}, + issn = {2045-2322}, + doi = {10.1038/s41598-022-14960-x}, + urldate = {2022-08-04}, + abstract = {Ride-pooling (or ride-sharing) services combine trips of multiple customers along similar routes into a single vehicle. The collective dynamics of the fleet of ride-pooling vehicles fundamentally underlies the efficiency of these services. In simplified models, the common features of these dynamics give rise to scaling laws of the efficiency that are valid across a wide range of street networks and demand settings. However, it is unclear how constraints of the vehicle fleet impact such scaling laws. Here, we map the collective dynamics of capacity-constrained ride-pooling fleets to services with unlimited passenger capacity and identify an effective fleet size of available vehicles as the relevant scaling parameter characterizing the dynamics. Exploiting this mapping, we generalize the scaling laws of ride-pooling efficiency to capacity-constrained fleets. We approximate the scaling function with a queueing theoretical analysis of the dynamics in a minimal model system, thereby enabling mean-field predictions of required fleet sizes in more complex settings. These results may help to transfer insights from existing ride-pooling services to new settings or service locations.}, + copyright = {2022 The Author(s)}, + langid = {english}, + keywords = {Statistical physics,Sustainability,thermodynamics and nonlinear dynamics}, + file = {/home/jung/Zotero/storage/QVBR4SKP/Zech et al. - 2022 - Collective dynamics of capacity-constrained ride-p.pdf;/home/jung/Zotero/storage/78864AQM/s41598-022-14960-x.html} +} + +@article{zwick2021a, + title = {Ride-{{Pooling Efficiency}} in {{Large}}, {{Medium-Sized}} and {{Small Towns}} -{{Simulation Assessment}} in the {{Munich Metropolitan Region}}}, + author = {Zwick, Felix and Kuehnel, Nico and Moeckel, Rolf and Axhausen, Kay W.}, + year = {2021}, + month = jan, + journal = {Procedia Computer Science}, + series = {The 12th {{International Conference}} on {{Ambient Systems}}, {{Networks}} and {{Technologies}} ({{ANT}}) / {{The}} 4th {{International Conference}} on {{Emerging Data}} and {{Industry}} 4.0 ({{EDI40}}) / {{Affiliated Workshops}}}, + volume = {184}, + pages = {662--667}, + issn = {1877-0509}, + doi = {10.1016/j.procs.2021.03.083}, + urldate = {2023-12-02}, + abstract = {This study introduces an autonomous ride-pooling service to six communities with varying population sizes and trip densities in the Munich Metropolitan Region. We analyze a) a laissez-faire scenario without additional policies, defining the modal shift through an incremental mode choice model and b) a draconian scenario in which each within-city car trip is replaced by ride-pooling. Results indicate a logarithmic increase in system efficiency with increasing trip densities. While the results confirm the potential of ride-pooling systems to reduce private car fleets drastically, a reduction of traveled km is identified for scenarios with more than 1,000 requests per km2 per day.}, + keywords = {New mobility,On-demand mobility,Pooling efficiency,Population density,Ride-sharing,Shared autonomous vehicles}, + file = {/home/jung/Zotero/storage/CG8DBTN4/Zwick et al. - 2021 - Ride-Pooling Efficiency in Large, Medium-Sized and.pdf;/home/jung/Zotero/storage/RURTPRSK/S1877050921007195.html} +} + +@article{zwick2022c, + title = {Shifts in Perspective: {{Operational}} Aspects in (Non-)Autonomous Ride-Pooling Simulations}, + shorttitle = {Shifts in Perspective}, + author = {Zwick, Felix and Kuehnel, Nico and H{\"o}rl, Sebastian}, + year = {2022}, + month = nov, + journal = {Transportation Research Part A: Policy and Practice}, + volume = {165}, + pages = {300--320}, + issn = {0965-8564}, + doi = {10.1016/j.tra.2022.09.001}, + urldate = {2023-12-02}, + abstract = {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.}, + keywords = {Electric vehicles,MATSim,Operations research,Pooled on-demand mobility,Ride-sharing,Ride-splitting}, + file = {/home/jung/Zotero/storage/74JDLRCE/Zwick et al. - 2022 - Shifts in perspective Operational aspects in (non.pdf;/home/jung/Zotero/storage/WUVRN69V/S0965856422002294.html} +} diff --git a/joss/paper.md b/joss/paper.md new file mode 100644 index 00000000..3fdf9439 --- /dev/null +++ b/joss/paper.md @@ -0,0 +1,72 @@ +--- +title: 'RidePy: A fast and modular framework for simulating ridepooling systems' +tags: + - Python + - simulation + - ridepooling + - mobility + - transport + - physics +authors: + - name: Felix Jung + orcid: 0000-0002-8689-3713 + affiliation: 1 + - name: Debsankha Manik + affiliation: 1 +affiliations: + - name: 'Chair of Network Dynamics, Institute of Theoretical Physics and Center for Advancing Electronics Dresden (cfaed), TUD Dresden University of Technology, 01062 Dresden, Germany' + index: 1 +date: 2023-12-04 +bibliography: paper.bib +--- + +# Summary + +RidePy enables fast computer simulations of on-demand mobility modes such as ridehailing or ridepooling. It strongly focuses on modeling the mobility service itself, rather than its customers or the environment. Through a combination of Python [@vanrossum1995], Cython [@cython] and C++ [@stroustrup2000], it offers ease of use at high performance. Its modular design makes customization easy, while the included modules allow for a quick start. + +# Statement of need + +An accelerating climate change and congested cities both call for an urgent change in the way we move [@winkler2023]. To reduce carbon dioxide emissions as well as the number of vehicles on the road, digitally managed on-demand mobility services such as ridehailing and ridepooling are explored in research [@santi2014; @engelhardt2019] and on the road. Unfortunately, physically experimenting with such services for research purposes is extremely cost- and time-intensive. However, the operational properties of such systems are largely predefined in terms of the scheduling backend that manages them. This makes it possible to replace physical experiments with computer simulations, substituting virtual vehicles for actual ones and modeling the incoming mobility demand by sampling either historic requests or synthetic distributions. Another advantage of simulations is that the degree to which they represent reality may be freely adjusted. This makes it possible to both answer concrete operational questions [@henao2019a; @ruch2020; @wilkes2021a; @zwick2021a; @zwick2022c; @lotze2022b; @deruijter2023] and investigate idealized system behavior, gaining deeper insights into the general properties of on-demand mobility systems [@tachet2017a; @herminghaus2019a; @molkenthin2020; @manik2020a; @zech2022; @muhle2023]. + +In this context, a simulation framework should appropriately allow for vastly different system sizes and degrees of realism. The system size incorporates the number of simulated vehicles as well as the extent of the space they operate on: A small system may consist of a single vehicle serving a network of just two nodes, while an example of a large system could be a fleet of several thousand vehicles operating on the street network of a large city. The degree of realism may be varied, for example, by sampling requests from either a uniform distribution or recorded mobility demand, or by operating on a continuous Euclidean plane versus a realistic city street network. +Another option is to adjust the constraints imposed, such as the time windows assigned to stops or the vehicles' seat capacities. + +Finally, an on-demand mobility simulation framework should be fast, easy to use and adaptable to various applications. + +A number of open-source simulation software projects are already being used to investigate on-demand mobility services. Some of them focus on microscopic modeling in realistic settings, through which concrete predictions for service operation are enabled, guiding urban planning. Prominent examples are MATSim [@horni2016], which performs agent-based simulations of individual inhabitants, and Eclipse SUMO [@lopez2018], a microscopic traffic simulator. Both rely on additional packages to model on-demand mobility, such as AMODEUS [@ruch2018] for MATSim and Jade [@behrisch2014] for SUMO. + +FleetPy [@engelhardt2022], a recently released on-demand mobility simulation, is primarily aimed at realistic modeling of the interactions between operators and users, specifically incorporating multiple operators. While its technical approach is similar to ours, integrating Python with fast Cython and C++ extensions, the project is predominantly focused on applied simulations, although its framework architecture promises to allow for adjustment of the model detail level. + +Perhaps the most idealized approach is taken by the Julia [@bezanson2017] package `RidePooling.jl` [@muehle2022] which was developed in support of a recent scientific contribution [@muehle2023]. + +A very different yet interesting route is taken by MaaSSim [@kucharski2022a], which models on-demand mobility in the realm of two-sided mobility platforms such as Uber [@uber] and Lyft [@lyft]. + +RidePy extends this landscape by providing a universal and fast ridepooling simulation framework that is highly customizable while still being easy to use. It is focused on modeling the behavior of a vehicle fleet while covering a broad scope in terms of system size and degree of realism. + +# Philosophy and usage + +RidePy simulates flexible mobility services based on *requests*, *dispatchers* and *vehicles*. The vehicles continuously move along routes defined by scheduled *stops*. At each stop, passengers are picked up or dropped off, leading to a change in seat occupancy aboard the vehicle. A `RequestGenerator` supplies requests for mobility that are submitted to the simulated service, consisting of origin and destination locations and optional constraints. A `dispatcher` processes these incoming requests. If a request cannot be fulfilled given the constraints (e.g., time windows, seat capacity), it is rejected upon submission. Otherwise, pick-up and drop-off stops are scheduled with a vehicle, respectively. + +All individual components of the simulation framework may be customized or replaced. This includes `RequestGenerator`s, `dispatcher`s, and the `TransportSpace` which the system operates on. Examples for `TransportSpace`s include the continuous Euclidean plane and arbitrary weighted graphs (e.g., street networks). Several components of RidePy are implemented in both pure Python and Cython/C++. While their pure Python versions are easier to understand, debug and modify, the Cython/C++ versions make large-scale simulations tractable. + +Running a RidePy simulation yields a sequence of `Event`s. The included analytics code consumes these events and returns two extensive Pandas [@wesmckinney2010] `DataFrame`s: `stops` and `requests`. `stops` contains all stops that have been visited by each vehicle, along with additional information such as the vehicles' passenger occupancy. `requests` similarly contains all requests that have entered the system, enriched with secondary information such as the time riders spent on the vehicle. + +Additional included tooling allows for the setup, parallel execution, and analysis of simulations at different parameters (parameter scans). This includes the serialization of all simulation data in JSON format [@json]. + +To ensure valid behavior, RidePy incorporates an extensive automated test suite [@pytest]. + +# Availability + +RidePy is available from PyPI [@ridepy-pypi]. The source code is hosted on GitHub [@ridepy-github]. Extensive documentation can be found on the project's webpage [@ridepy-doc]. + +# Acknowledgements + +We kindly thank Philip Marszal, Matthias Dahlmanns, Knut Heidemann, Malte Schröder, and Marc Timme for their input and advice. + +This project was partially supported by the Bundesministerium für Bildung und Forschung (BMBF, German Federal Ministry of Education and Research) under grant No. 16ICR01 and by the Bundesministerium für Digitales und Verkehr (BMDV, German Federal Ministry for Digital and Transport) as part of the innovation initiative mFund under grant No. 19F1155A. + +# Competing interests + +Debsankha Manik was employed at MOIA GmbH when this research was conducted. MOIA GmbH neither sponsored nor endorses his research. + +# References