Bibliografías temáticas / Ridesplitting

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Literatura académica sobre el tema "Ridesplitting"

Autor: Grafiati
Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Ridesplitting"

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Cahyo, Anggit, Nahry, and Helen Burhan. "Mode choice model analysis between ridesouring and ridesplitting service in DKI Jakarta." MATEC Web of Conferences 270 (2019): 03013. http://dx.doi.org/10.1051/matecconf/201927003013.

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Beside the ridesoucing service, ridesplitting service is also offered by Transport Network Companies (TNC). The ridesplitting service have more benefit than ridesourcing because it is using the concept of carsharing. The current condition for ridesplitting service is not popular and only have small demand than ridesourcing service. This study aims to establish a mode choice model between ridesourcing and ridesplitting service in DKI Jakarta and to estimate the potential of demand shifting from ridesourcing to ridesplitting service in DKI Jakarta. The mode choice model is developed from binary logit model with stated preference survey using fare saving, additional time travel and security presented by gender parameter of ridesplitting service. the sensitivity of logit model show that highest sensitivity rate to increase mode switching to ridesplitting service is in 20% to 50% fare saving level. The probability of current condition to switch to ridesplitting service is 20%.
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Li, Xiaomei, Yiwen Zhang, Zijie Yang, Yijun Zhu, Cihang Li, and Wenxiang Li. "Modeling Choice Behaviors for Ridesplitting under a Carbon Credit Scheme." Sustainability 15, no. 16 (2023): 12241. http://dx.doi.org/10.3390/su151612241.

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Ridesplitting, a form of shared ridesourcing service, has the potential to significantly reduce emissions. However, its current adoption rate among users remains relatively low. Policies such as carbon credit schemes, which offer rewards for emission reduction, hold great promise in promoting ridesplitting. This study aimed to quantitatively analyze the choice behaviors for ridesplitting under a carbon credit scheme. First, both the socio-demographic and psychological factors that may influence the ridesplitting behavioral intention were identified based on the theory of planned behavior, technology acceptance model, and perceived risk theory. Then, a hybrid choice model of ridesplitting was established to model choice behaviors for ridesplitting under a carbon credit scheme by integrating both structural equation modeling and discrete choice modeling. Meanwhile, a stated preference survey was conducted to collect the socio-demographic and psychological information and ridesplitting behavioral intentions of transportation network company (TNC) users in 12 hypothetical scenarios with different travel distances and carbon credit prices. Finally, the model was evaluated based on the survey data. The results show that attitudes, subjective norms, perceived behavioral control, low-carbon values, and carbon credit prices have significant positive effects on the choice behavior for ridesplitting. Specifically, increasing the carbon credit price could raise the probability of travelers choosing ridesplitting. In addition, travelers with higher low-carbon values are usually more willing to choose ridesplitting and are less sensitive to carbon credit prices. The findings of this study indicate that a carbon credit scheme is an effective means to incentivize TNC users to choose ridesplitting.
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Wang, Jincheng, Qunqi Wu, Zilin Chen, Yilong Ren, and Yaqun Gao. "Exploring the Factors of Intercity Ridesplitting Based on Observed and GIS Data: A Case Study in China." ISPRS International Journal of Geo-Information 10, no. 9 (2021): 622. http://dx.doi.org/10.3390/ijgi10090622.

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Ridesplitting, a form of ridesourcing in which riders with similar origins and destinations are matched, is an effective mode of sustainable transportation. In recently years, ridesplitting has spread rapidly worldwide and plays an increasingly important role in intercity travel. However, intercity ridesplitting has rarely been studied. In this paper, we use observe intercity ridesplitting data between Yinchuan and Shizuishan in China and building environment data based on a geographic information system (GIS) to analyse temporal, spatial and other characteristics. Then, we divide the study area into grids and explore the contributing factors that affect the intercity ridesplitting matching success rate. Based on these significant factors, we develop a binary logistic regression (BLR) model and predict the intercity ridesplitting matching success rate. The results indicate that morning peak, evening peak, weekends and weekdays, precipitation and snowfall, population density, some types of points of interest (POI), travel time and the advance appointment time are significant factors. In addition, the prediction accuracy of the model is more than 78%, which shows that the factors studied in this paper have good explanatory power. The results of this study can help in understanding the characteristics of intercity ridesplitting and provide a reference for improving the intercity ridesplitting matching success rate.
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Li, Xinghua, Feiyu Feng, Wei Wang, Cheng Cheng, Tianzuo Wang, and Pengcheng Tang. "Structure Analysis of Factors Influencing the Preference of Ridesplitting." Journal of Advanced Transportation 2021 (February 24, 2021): 1–8. http://dx.doi.org/10.1155/2021/8820701.

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Ridesplitting is a new form of for-hire service that riders with similar origins and destinations are matched to the same vehicle in real-time via Internet. However, the market share of ridesplitting only accounts for a small fraction of total travel. Understanding cognitive factors affecting ridesplitting preference would be helpful in designing its market measures, regulations, and incentives to achieve high-level customer attractions. This paper identifies the cognitive determinants affecting ridesplitting preference and their inner relationships via the structural equation model. The data from an online survey conducted in Shanghai were implemented for model calibration. The modal fitness results are reasonable, and the path coefficients are significant, exhibiting that the proposed hypothesis cannot be rejected. Specifically, attitude towards incentives and management issues, perceived benefit, and perceived usefulness appear to be strong active driving forces that encourage the desire to adopt ridesplitting.
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Du, Mingyang, Lin Cheng, Xuefeng Li, Qiyang Liu, and Jingzong Yang. "Spatial variation of ridesplitting adoption rate in Chicago." Transportation Research Part A: Policy and Practice 164 (October 2022): 13–37. http://dx.doi.org/10.1016/j.tra.2022.07.018.

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Beojone, Caio Vitor, and Nikolas Geroliminis. "A dynamic multi-region MFD model for ride-sourcing with ridesplitting." Transportation Research Part B: Methodological 177 (November 2023): 102821. http://dx.doi.org/10.1016/j.trb.2023.102821.

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Li, Wenxiang, Lei Wang, Ziyuan Pu, Long Cheng, and Linchuan Yang. "What determines the real-world CO2 emission reductions of ridesplitting trips?" Travel Behaviour and Society 35 (April 2024): 100734. http://dx.doi.org/10.1016/j.tbs.2023.100734.

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Tu, Meiting, Wenxiang Li, Olivier Orfila, Ye Li, and Dominique Gruyer. "Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu." Transportation Research Part D: Transport and Environment 93 (April 2021): 102776. http://dx.doi.org/10.1016/j.trd.2021.102776.

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Zhu, Zheng, Xiaoran Qin, Jintao Ke, Zhengfei Zheng, and Hai Yang. "Analysis of multi-modal commute behavior with feeding and competing ridesplitting services." Transportation Research Part A: Policy and Practice 132 (February 2020): 713–27. http://dx.doi.org/10.1016/j.tra.2019.12.018.

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Chen, Xiqun (Michael), Majid Zahiri, and Shuaichao Zhang. "Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach." Transportation Research Part C: Emerging Technologies 76 (March 2017): 51–70. http://dx.doi.org/10.1016/j.trc.2016.12.018.

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Tesis sobre el tema "Ridesplitting"

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Tu, Meiting. "Optimisation des services de "ridesourcing" pour le déploiement futur des véhicules autonomes et connectés en milieu urbain." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG048.

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Les services de transport à la demande sont de plus en plus populaires en raison de leur commodité. Cependant, certaines études font apparaitre que ces services pourraient augmenter les congestions et le niveau de pollution. Le ridesplitting, un nouveau service de mobilité partagée, est un moyen plus durable de se déplacer pour améliorer l'efficacité des transports et réduire les émissions de polluants. Dans ce contexte, ce travail propose un cadre d'optimisation pour un Système de Mobilité Partagée (SMP). L'originalité et les aspects innovants développés dans cette thèse sont aussi bien théoriques et méthodologiques, qu’appliqués. Du point de vue théorique et méthodologique, le cadre proposé pour le SMP fournit une méthodologie systématique et générique pour la modélisation et la simulation. Les algorithmes d’IA proposés permettent d’analyser et de mieux comprendre les comportements de déplacement des usagers et leur modélisation spatio-temporelle. Pour ce qui est de l’application pratique de ces travaux, le SMP proposé peut améliorer significativement les services de ridesplitting tout en réduisant l’empreinte carbone. De plus, ce SMP est facilement extrapolable aux CAV et aux futurs systèmes de mobilités<br>On-demand ridesourcing services have become increasingly popular due to their convenience. There are some debates claiming that ridesourcing services could increase congestion and pollution. Ridesplitting, a new shared mobility service, is a more sustainable travel mode for improving traffic efficiency and reducing air pollution. Therefore, the motivation of this study is to propose an optimization framework for the shared mobility system (SMS). The originality and innovative aspects of this dissertation could be summarized according to 2 perspectives. For the value of theory and methodology, the proposed framework for the SMS could provide a systematic methodology for the modelling and simulation. The proposed artificial intelligent algorithms could provide a better understanding for the researches of travel behaviors analysis and spatiotemporal modelling. For the value of practical application, the proposed shared mobility system could help improve ridesplitting service to build a low carbon transport, which could incorporate CAVs for the future mobility
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Actas de conferencias sobre el tema "Ridesplitting"

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Zhu, Jiangtao, Dong Mo, and Xiqun (Michael) Chen. "A Grouping Approach to Ridesplitting Optimization." In 20th COTA International Conference of Transportation Professionals. American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784482933.394.

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Chen, Zilin, Yilong Ren, Xinrui Zheng, and Xiang Fu. "Impact of COVID-19 on Spatiotemporal Factors Affecting Ridesplitting Demand." In 22nd COTA International Conference of Transportation Professionals. American Society of Civil Engineers, 2022. http://dx.doi.org/10.1061/9780784484265.084.

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Zhang, Fangda, Shannon Roberts, and Claudia Goldman. "A Survey Study Measuring People's Preferences Towards Automated and Non-Automated Ridesplitting." In Driving Assessment Conference. University of Iowa, 2019. http://dx.doi.org/10.17077/drivingassessment.1680.

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TU, Meiting, Ye LI, Wenxiang LI, et al. "Improving Ridesplitting Service Using Optimization Procedures on Shareability Network: A Case Study of Chengdu, China." In 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8917402.

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Chen, Zilin, Haiyang Yu, and Runkun Liu. "Exploring the Spatiotemporal Factors of Ridesplitting Demand Based on the Geographically and Temporally Weighted Regression." In 2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). IEEE, 2021. http://dx.doi.org/10.1109/tocs53301.2021.9688587.

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