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Journal articles on the topic 'Ridesplitting'

Author: Grafiati

Published: 25 May 2024

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1

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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2

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 (August 10, 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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3

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 (September 17, 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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Yang, Hongtai, Peng Luo, Chaojing Li, Guocong Zhai, and Anthony G. O. Yeh. "Nonlinear effects of fare discounts and built environment on ridesplitting adoption rates." Transportation Research Part A: Policy and Practice 169 (March 2023): 103577. http://dx.doi.org/10.1016/j.tra.2022.103577.

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12

Liu, Hao, Saipraneeth Devunuri, Lewis Lehe, and Vikash V. Gayah. "Scale effects in ridesplitting: A case study of the City of Chicago." Transportation Research Part A: Policy and Practice 173 (July 2023): 103690. http://dx.doi.org/10.1016/j.tra.2023.103690.

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13

Zhang, Zhe, Kun Gao, Hong-Di He, Shaohua Cui, Liyang Hu, Qing Yu, and Zhong-Ren Peng. "Environmental impacts of ridesplitting considering modal substitution and associations with built environment." Transportation Research Part D: Transport and Environment 130 (May 2024): 104160. http://dx.doi.org/10.1016/j.trd.2024.104160.

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14

Xu, Yiming, Xiang Yan, Xinyu Liu, and Xilei Zhao. "Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships." Transportation Research Part A: Policy and Practice 144 (February 2021): 170–88. http://dx.doi.org/10.1016/j.tra.2020.12.005.

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15

Li, Wenxiang, Ziyuan Pu, Yuanyuan Li, and Meiting Tu. "How does ridesplitting reduce emissions from ridesourcing? A spatiotemporal analysis in Chengdu, China." Transportation Research Part D: Transport and Environment 95 (June 2021): 102885. http://dx.doi.org/10.1016/j.trd.2021.102885.

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16

Li, Wenxiang, Ziyuan Pu, Ye Li, and Xuegang (Jeff) Ban. "Characterization of ridesplitting based on observed data: A case study of Chengdu, China." Transportation Research Part C: Emerging Technologies 100 (March 2019): 330–53. http://dx.doi.org/10.1016/j.trc.2019.01.030.

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17

Huang, Guan, Zhan Zhao, and A. G. O. Yeh. "How shareable is your trip? A path-based analysis of ridesplitting trip shareability." Computers, Environment and Urban Systems 110 (June 2024): 102120. http://dx.doi.org/10.1016/j.compenvurbsys.2024.102120.

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18

Rojas-Rueda, David, Mark J. Nieuwenhuijsen, Haneen Khreis, and Howard Frumkin. "Autonomous Vehicles and Public Health." Annual Review of Public Health 41, no. 1 (April 2, 2020): 329–45. http://dx.doi.org/10.1146/annurev-publhealth-040119-094035.

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Autonomous vehicles (AVs) have the potential to shape urban life and significantly modify travel behaviors. “Autonomous technology” means technology that can drive a vehicle without active physical control or monitoring by a human operator. The first AV fleets are already in service in US cities. AVs offer a variety of automation, vehicle ownership, and vehicle use options. AVs could increase some health risks (such as air pollution, noise, and sedentarism); however, if proper regulated, AVs will likely reduce morbidity and mortality from motor vehicle crashes and may help reshape cities to promote healthy urban environments. Healthy models of AV use include fully electric vehicles in a system of ridesharing and ridesplitting. Public health will benefit if proper policies and regulatory frameworks are implemented before the complete introduction of AVs into the market.

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19

Wang, Ze, Xiaowei Chen, and Xiqun (Michael) Chen. "Ridesplitting is shaping young people’s travel behavior: Evidence from comparative survey via ride-sourcing platform." Transportation Research Part D: Transport and Environment 75 (October 2019): 57–71. http://dx.doi.org/10.1016/j.trd.2019.08.017.

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20

Tu, Meiting, Ye Li, Wenxiang Li, Minchao Tu, Olivier Orfila, and Dominique Gruyer. "Improving ridesplitting services using optimization procedures on a shareability network: A case study of Chengdu." Technological Forecasting and Social Change 149 (December 2019): 119733. http://dx.doi.org/10.1016/j.techfore.2019.119733.

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21

Zhang, Zhe, Kun Gao, Hong-Di He, Jin-Ming Yang, Ruo Jia, and Zhong-Ren Peng. "How do travel characteristics of ridesplitting affect its benefits in emission reduction? evidence from Chengdu." Transportation Research Part D: Transport and Environment 123 (October 2023): 103912. http://dx.doi.org/10.1016/j.trd.2023.103912.

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22

Zhang, Xin, Shiquan Zhong, Ning Jia, Shuai Ling, Wang Yao, and Shoufeng Ma. "A barrier to the promotion of app-based ridesplitting: Travelers’ ambiguity aversion in mode choice." Transportation Research Part A: Policy and Practice 181 (March 2024): 103971. http://dx.doi.org/10.1016/j.tra.2024.103971.

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23

Zheng, Hongyu, Xiaowei Chen, and Xiqun Michael Chen. "How Does On-Demand Ridesplitting Influence Vehicle Use and Purchase Willingness? A Case Study in Hangzhou, China." IEEE Intelligent Transportation Systems Magazine 11, no. 3 (2019): 143–57. http://dx.doi.org/10.1109/mits.2019.2919503.

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24

Huang, Guan, Si Qiao, and Anthony Gar-On Yeh. "Spatiotemporally heterogeneous willingness to ridesplitting and its relationship with the built environment: A case study in Chengdu, China." Transportation Research Part C: Emerging Technologies 133 (December 2021): 103425. http://dx.doi.org/10.1016/j.trc.2021.103425.

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25

Soria, Jason, Ying Chen, and Amanda Stathopoulos. "K-Prototypes Segmentation Analysis on Large-Scale Ridesourcing Trip Data." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (July 7, 2020): 383–94. http://dx.doi.org/10.1177/0361198120929338.

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Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they affect travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examined emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data were matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcing’s role in Chicago’s mobility system. The goal was to investigate the systematic variations in patronage of ridehailing. K-prototypes was utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output was a classification of the data into several clusters called prototypes. Six ridesourcing prototypes were identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses the implications of the identified clusters related to affordability, equity, and competition with transit.

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26

Liu, Xinghua, Wenxiang Li, Ye Li, Jing Fan, and Zhiyong Shen. "Quantifying Environmental Benefits of Ridesplitting based on Observed Data from Ridesourcing Services." Transportation Research Record: Journal of the Transportation Research Board, March 12, 2021, 036119812199782. http://dx.doi.org/10.1177/0361198121997827.

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The increasing emissions from the transportation sector pose substantial hazards to the environment and human health around the world. With the rapid development of information and communication technologies, ridesplitting, a form of ridesourcing service accessed via smartphone applications, enables passengers with similar origins and destinations to be matched to the same driver and share the ride. This is regarded as a promising travel mode that could mitigate air pollution. However, because of a lack of quantitative analysis, the environmental benefits of ridesplitting have not been rigorously justified. As vast amounts of observed data of ridesourcing have become increasingly available, this study quantifies the environmental benefits of ridesplitting based on the global positioning system (GPS) trajectory and trip order datasets of DiDi Chuxing in Chengdu, China. First, the saved distances of ridesplitting are calculated by analyzing the travel distances of both ridesplitting trips and the corresponding trips under non-ridesplitting conditions. Then, the emission factors of CO, NOx, and HC are estimated by a localized MOVES model. Combining the saved distances and emission factors, the emission reductions from each ridesplitting trip can be calculated. The results show that ridesplitting can decrease the travel distance by 22% on average compared with non-ridesplitting. As a consequence, the average emission reductions per ridesplitting trip are 10.601 g of CO, 0.691 g of NOx, and 1.424 g of HC, respectively. This study provides a better understanding of the environmental benefits of ridesplitting and theoretical guidance for the government’s decision-making in green transport planning.

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27

Zhang, Fangda, Shannon C. Roberts, and Claudia V. Goldman. "How Do People Prefer Ridesplitting? A Survey Study Focusing on Passenger, Matching, and Trip Characteristics." Proceedings of the Human Factors and Ergonomics Society Annual Meeting, December 8, 2023. http://dx.doi.org/10.1177/21695067231195826.

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Ridesplitting services have gained great popularity in recent years. Past research has stressed that passengers, social matching, and trip characteristics are users’ top concerns when choosing such services. The present study sought to uncover potential users’ preferences toward ridesplitting in terms of the three aspects. We conducted a survey study and leveraged logit and multivariate ordinal regression models to analyze the data. Our results show that most respondents preferred splitting a ride with others that they’ve known and that they cared about passenger’s characteristics. Their expectations of trip length and time were quantitatively revealed. Sociodemographic factors exerted impact in such a way that users of certain groups were more conservative toward ridesplitting services. To better deploy ridesplitting and increase its adoption among users, we would recommend that future services consider users’ preferences and sociodemographic information when matching different people. An accurately estimated travel time also appears to be desired.

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28

Abkarian, Hoseb, Ying Chen, and Hani S. Mahmassani. "Understanding Ridesplitting Behavior with Interpretable Machine Learning Models Using Chicago Transportation Network Company Data." Transportation Research Record: Journal of the Transportation Research Board, September 11, 2021, 036119812110363. http://dx.doi.org/10.1177/03611981211036363.

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As congestion levels increase in cities, it is important to analyze people’s choices of different services provided by transportation network companies (TNCs). Using machine learning techniques in conjunction with large TNC data, this paper focuses on uncovering complex relationships underlying ridesplitting market share. A real-world dataset provided by TNCs in Chicago is used in analyzing ridesourcing trips from November 2018 to December 2019 to understand trends in the city. Aggregated origin–destination trip-level characteristics, such as mean cost, mean time, and travel time reliability, are extracted and combined with origin–destination community-level characteristics. Three tree-based algorithms are then utilized to model the market share of ridesplitting trips. The most significant factors are extracted as well as their marginal effect on ridesplitting behavior, using partial dependency plots for interpretation of the machine learning model results. The results suggest that, overall, community-level factors are as or more important than trip-level characteristics. Additionally, the percentage of White people highly affects ridesplitting market share as well as the percentage of bachelor’s degree holders and households with two people residing in them. Travel time reliability and cost variability are also deemed more important than travel time and cost savings. Finally, the potential impact of taxes, crimes, cultural differences, and comfort is discussed in driving the market share, and suggestions are presented for future research and data collection attempts.

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29

Zhang, Xin, Shiquan Zhong, Ning Jia, Wang Yao, and Shoufeng Ma. "A Barrier to the Promotion of Ridesplitting: Travelers' Ambiguity Aversion in App-Based Ridesplitting." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4250283.

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30

Lu, Qing-Long, Moeid Qurashi, and Constantinos Antoniou. "A ridesplitting market equilibrium model with utility-based compensation pricing." Transportation, September 28, 2022. http://dx.doi.org/10.1007/s11116-022-10339-z.

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AbstractThe paper develops a theoretic equilibrium model for ridesplitting markets with specific considerations of origin-destination demand patterns, competition with other transport modes, characteristics of en route matching, and spatial allocation of ridesplitting vehicles, to adequately portray the intertwined relationships between the endogenous variables and decisions. The operation property of the market under distance-based unified pricing is analyzed through the response of system performance indicators to the decisions. Moreover, a gradient descent algorithm is derived to find optimal operating strategies in the monopoly scenario and social optimum scenario. Leveraging the tight connection between trip’s utility and level of service (LoS), the paper then proposes a utility-based compensation pricing method to alleviate the inequity issue in ridesplitting, which results from the variance in waiting time and detour time and the implementation of unified pricing. Specifically, the trip fare of those with an initial utility smaller than a threshold will be compensated following a predefined compensation function. We compare its effectiveness and influence in different scenarios through numerical experiments at Munich. The results show that the proposed pricing method can improve the LoS and equity without losing any profit and welfare, and can even achieve increments in maximum profit and social welfare under certain conditions.

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31

Li, Yafei, Huijun Sun, Ying Lv, and Ximing Chang. "Ridesplitting demand prediction via spatiotemporal multi-graph convolution network." Expert Systems with Applications, January 2024, 123207. http://dx.doi.org/10.1016/j.eswa.2024.123207.

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32

Li, Yafei, Huijun Sun, and Ying Lv. "Collaborative matching of ridesplitting and connection in the ridesourcing market." Fundamental Research, August 2021. http://dx.doi.org/10.1016/j.fmre.2021.07.004.

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33

Wang, Lei, Wenxiang Li, Jinxian Weng, Dong Zhang, and Wanjing Ma. "Do low-carbon rewards incentivize people to ridesplitting? Evidence from structural analysis." Transportation, June 22, 2022. http://dx.doi.org/10.1007/s11116-022-10302-y.

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34

Qu, Boting, Xinyu Ren, Jun Feng, and Xin Wang. "A Dynamic Ridesplitting Method With Potential Pick-Up Probability Based on GPS Trajectories." IEEE Transactions on Intelligent Transportation Systems, 2021, 1–17. http://dx.doi.org/10.1109/tits.2021.3095765.

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35

Chen, Xiaowei, Hongyu Zheng, Ze Wang, and Xiqun Chen. "Exploring impacts of on-demand ridesplitting on mobility via real-world ridesourcing data and questionnaires." Transportation, August 27, 2018. http://dx.doi.org/10.1007/s11116-018-9916-1.

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36

Hansen, Todd, and Ipek Nese Sener. "Strangers On This Road We Are On: A Literature Review of Pooling in On-Demand Mobility Services." Transportation Research Record: Journal of the Transportation Research Board, September 27, 2022, 036119812211238. http://dx.doi.org/10.1177/03611981221123801.

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Ridepooling service options introduced by transportation network companies (TNCs) and microtransit companies provide opportunities to increase shared-ride trips in vehicles, thereby improving congestion and environmental factors. This paper reviews the existing literature available on ridepooling and related services, specifically focusing on pooling options available from on-demand transportation companies. The paper summarizes the existing knowledge on the use of pooled-ride services, factors in travel mode service options for customers, available policy and planning strategies to incentivize sharing vehicles, and effects of the COVID-19 pandemic on shared-ride travel. Overall, research shows that ridepooling options are more likely to be considered by public transit users who have lower household incomes, while ridesourcing users of upper-class backgrounds are less likely to consider moving to a shared-ride service. Travel time and trip cost are the most important factors for travelers determining whether to use a ridesplitting or microtransit service rather than a ride-alone ridesourced trip. Existing policy and planning tools targeting pooled travel or TNCs can be expanded on and specified for on-demand ridepooling services, such as offering better incentives to use shared vehicles and increased access to curb areas or travel lanes, but the most effective strategies will include increasing the user costs for parking or riding alone.

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