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Ridesourcing systems: A framework and review

Hai Wang, Hai Yang
2019 Transportation Research Part B: Methodological  
On the demand side, passengers are sensitive to the price and quality of the service.  ...  These features motivate various operational strategies, such as "dynamic or surge pricing/wage," by which the platform adjusts both the prices and wages dynamically depending on real-time supply and demand  ...  We also express our sincere appreciation to the seven anonymous referees for their invaluable comments and suggestions.  ... 
doi:10.1016/j.trb.2019.07.009 fatcat:m5rxzhsxsfaunoep77nsqdhbny

Scanning the Issue

Azim Eskandarian
2021 IEEE transactions on intelligent transportation systems (Print)  
Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV control.  ...  This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organizing recent research into a hierarchical taxonomy ranging from lowlevel  ...  Estimating Traffic Flow in Large Road Networks Based on Multi-Source Traffic Data P. Wang, J. Lai, Z. Huang, Q. Tan, and T.  ... 
doi:10.1109/tits.2021.3104912 fatcat:jy6vxumv2bg6feqdzpffik2y4e

Tackling Climate Change with Machine Learning [article]

David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli (+6 others)
2019 arXiv   pre-print
Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate.  ...  We call on the machine learning community to join the global effort against climate change.  ...  The authors gratefully acknowledge support from National Science Foundation grant 1803547, the Center for Climate and Energy Decision Making through a cooperative agreement between the National Science  ... 
arXiv:1906.05433v2 fatcat:ykmqsivkbfcazaz3wl5f7srula

A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning [article]

Marina Haliem, Ganapathy Mani, Vaneet Aggarwal, Bharat Bhargava
2021 arXiv   pre-print
or reject rides based on their set of preferences with respect to pricing and delay windows, vehicle type and carpooling preferences, and (4) based on demand prediction, our approach re-balances idle vehicles  ...  In this paper, we present a dynamic, demand aware, and pricing-based vehicle-passenger matching and route planning framework that (1) dynamically generates optimal routes for each vehicle based on online  ...  In this paper, we present a dynamic, demand-aware, and pricing-based vehicle-passenger matching and route-planning framework that scales up to accommodate more than two rides per vehicle (up to the maximum  ... 
arXiv:2010.01755v2 fatcat:4fbgj7njxjg6rovnyzy5exfixy

Sharing behavior in ride-hailing trips: A machine learning inference approach

Morteza Taiebat, Elham Amini, Ming Xu
2022 Transportation Research Part D: Transport and Environment  
Using a novel dataset from all ride-hailing trips in Chicago in 2019, we show that the willingness of riders to request a shared ride has monotonically decreased from 27.0% to 12.8% throughout the year  ...  Our findings shed light on sharing behavior in ride-hailing trips and can help devise strategies that increase shared ride-hailing, especially as the demand recovers from pandemic.  ...  Our findings shed light on sharing behavior in ride-hailing trips and can help TNCs, urban planners, and policymakers to devise better strategies and targeted pricing mechanisms to increase sharing in  ... 
doi:10.1016/j.trd.2021.103166 fatcat:wuc4lfkfhjaubmbxeizytjbtbi

Sociotechnical Specification for the Broader Impacts of Autonomous Vehicles [article]

Thomas Krendl Gilbert, Aaron J. Snoswell, Michael Dennis, Rowan McAllister, Cathy Wu
2022 arXiv   pre-print
Autonomous Vehicles (AVs) will have a transformative impact on society.  ...  This comprises a problem of sociotechnical specification: the need to distinguish which essential features of the transportation system are in or out of scope for AV development.  ...  There may well be cross-regional consequences of this strategy, as the urban sprawl we see now is not only a result of people moving to suburbs, but the housing supply adapting to that demand.  ... 
arXiv:2205.07395v1 fatcat:uyls7b5k6vg4zdubrt2tkqvy6a

Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges

Xiaochen Fan, Chaocan Xiang, Liangyi Gong, Xin He, Yuben Qu, Saeed Amirgholipour, Yue Xi, Priyadarsi Nanda, Xiangjian He
2020 CCF Transactions on Pervasive Computing and Interaction  
In recent years, the rapid uptake of the Internet of Vehicles and the rising pervasiveness of mobile services have produced unprecedented amounts of data to serve traffic sensing and prediction applications  ...  In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.  ...  It also called traffic demand prediction, which is a critical component in taxi services and ride-hailing services.  ... 
doi:10.1007/s42486-020-00039-x fatcat:c3c2b3fvpzdqdlxy2ke7ckxlpu

A simulation-based evaluation of a Cargo-Hitching service for E-commerce using mobility-on-demand vehicles [article]

Andre Alho, Takanori Sakai, Simon Oh, Cheng Cheng, Ravi Seshadri, Wen Han Chong, Yusuke Hara, Julia Caravias, Lynette Cheah, Moshe Ben-Akiva
2020 arXiv   pre-print
This research aims to evaluate the use of Mobility-On-Demand services to perform same-day parcel deliveries.  ...  One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo-hitching.  ...  The authors confirm contribution to the paper as follows: study conception and design: André Romano Alho, Ravi Seshadri, Lynette Cheah, Moshe-Ben Akiva; data collection: Julia Caravias, André Romano Alho  ... 
arXiv:2010.11585v1 fatcat:23jdsmuj6vewzoy2qp2b2huv5e

Simulating a rich ride-share mobility service using agent-based models

Pau Segui-Gasco, Haris Ballis, Vittoria Parisi, David G. Kelsall, Robin J. North, Didac Busquets
2019 Transportation  
Mobility as a Service (MaaS) is the integrated and on-demand offering of new mode-sharing transport schemes, such as ride-share, car-share or car-pooling.  ...  We show how the simulation tools complement each other to deliver a superior Autonomous Mobility on Demand (AMoD) modelling capability.  ...  Acknowledgements The authors would like to acknowledge the help and support from fellow members  ... 
doi:10.1007/s11116-019-10012-y fatcat:n6ci7l3355e63b7xxumnttrlre

Computing the Relative Value of Spatio-Temporal Data in Wholesale and Retail Data Marketplaces [article]

Santiago Andrés Azcoitia, Marius Paraschiv, Nikolaos Laoutaris
2020 arXiv   pre-print
Overall, we show that simplistic but popular approaches for estimating the relative value of data, such as using volume, or the "leave-one-out" heuristic, are inaccurate.  ...  In such marketplaces, several sources may need to combine their data in order to meet the requirements of different applications.  ...  is a reasonable and fair approach but need not be the only one.  ... 
arXiv:2002.11193v2 fatcat:2e4zeozjs5fmpavjdf6p2rjmly

A Simulation-Based Evaluation of a Cargo-Hitching Service for E-Commerce Using Mobility-on-Demand Vehicles

André Romano Alho, Takanori Sakai, Simon Oh, Cheng Cheng, Ravi Seshadri, Wen Han Chong, Yusuke Hara, Julia Caravias, Lynette Cheah, Moshe Ben-Akiva
2021 Future Transportation  
E-commerce demand carrier data collected in Singapore are used to characterize simulated parcel delivery demand.  ...  One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo hitching.  ...  In other words, real-time effects of spatial demand and supply imbalances on ride prices (i.e., dynamic pricing or surge pricing) are not modeled.  ... 
doi:10.3390/futuretransp1030034 fatcat:s65x2yfmf5czfhub4waab2xzre

The Value of Time in the United States: Estimates from Nationwide Natural Field Experiments

Ariel Goldszmidt, John A. List, Ian Muir, Robert D. Metcalfe, V. Kerry Smith, Jenny Wang
2020 Social Science Research Network  
We use random variation in both wait times and prices to estimate a consumer's VOT with a data set of more than 14 million observations across consumers in U.S. cities.  ...  Economists in the 1960s began to focus on the value of non-work time, pioneering a deep literature exploring the optimal allocation and value of time.  ...  On a rideshare platform, one possible approach would be to run a multi-modal waiting time and price experiment, and to model the passenger's choice of not only whether to take a ride, but also of which  ... 
doi:10.2139/ssrn.3748629 fatcat:godyw4epsncuvb3zoiszffa5ve

A review of public transport economics

Daniel Hörcher, Alejandro Tirachini
2021 Economics of Transportation  
We discuss key findings on optimal capacity provision, pricing, cost recovery and subsidies, externalities, private operations, public service regulation, and cross-cutting subjects, such as interlinks  ...  with urban economics, political economy, and emerging mobility technologies.  ...  (2016) acknowledged the theoretical merits of the multiproduct approach but pointed out that its empirical application is very demanding due to the need for disaggregate output data. pricing undeniable  ... 
doi:10.1016/j.ecotra.2021.100196 fatcat:qospdhlmo5fw5lf7xfldd6phbe

(So) Big Data and the transformation of the city

Gennady Andrienko, Natalia Andrienko, Chiara Boldrini, Guido Caldarelli, Paolo Cintia, Stefano Cresci, Angelo Facchini, Fosca Giannotti, Aristides Gionis, Riccardo Guidotti, Michael Mathioudakis, Cristina Ioana Muntean (+6 others)
2020 International Journal of Data Science and Analytics  
In this paper, we provide a wide perspective on the role that big data have in reshaping cities.  ...  We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.  ...  Availability of data and materials The data and methods that support the findings described in this paper can be found in the SoBigData catalogue at https://sobigdata.d4science.org/catalogue-sobigdata.  ... 
doi:10.1007/s41060-020-00207-3 fatcat:k4eh5k2epjblzf6myc7tiw5gam

Mobility Modeling and Prediction in Bike-Sharing Systems

Zidong Yang, Ji Hu, Yuanchao Shu, Peng Cheng, Jiming Chen, Thomas Moscibroda
2016 Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services - MobiSys '16  
In this paper, for the first time, we propose a spatio-temporal bicycle mobility model based on historical bike-sharing data, and devise a traffic prediction mechanism on a per-station basis with sub-hour  ...  Evaluation results show an 85 percentile relative error of 0.6 for both check in and check out prediction.  ...  Acknowledgment This work is supported in part by the National Basic Research Program (973 Program) under Grant 2015CB352500, and National Program for Special Support of Top Notch Young Professionals.  ... 
doi:10.1145/2906388.2906408 dblp:conf/mobisys/YangHSCCM16 fatcat:k2x77dicavft7mbhos7jg5syfu
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