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Keywords = Transportation Network Companies (TNCs)

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22 pages, 5960 KiB  
Article
Application of Integrated Geospatial Analysis and Machine Learning in Identifying Factors Affecting Ride-Sharing Before/After the COVID-19 Pandemic
by Afshin Allahyari and Farideddin Peiravian
ISPRS Int. J. Geo-Inf. 2025, 14(8), 291; https://doi.org/10.3390/ijgi14080291 - 28 Jul 2025
Viewed by 287
Abstract
Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after [...] Read more.
Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after a significant delay following the lockdown. This raises the question of what determinants shape ride-pooling in the post-pandemic era and how they spatially influence shared ride-hailing compared to the pre-pandemic period. To address this gap, this study employs geospatial analysis and machine learning to examine the factors affecting ride-pooling trips in pre- and post-pandemic periods. Using over 66 million trip records from 2019 and 43 million from 2023, we observe a significant decline in shared trip adoption, from 16% to 2.91%. The results of an extreme gradient boosting (XGBoost) model indicate a robust capture of non-linear relationships. The SHAP analysis reveals that the percentage of the non-white population is the dominant predictor in both years, although its influence weakened post-pandemic, with a breakpoint shift from 78% to 90%, suggesting reduced sharing in mid-range minority areas. Crime density and lower car ownership consistently correlate with higher sharing rates, while dense, transit-rich areas exhibit diminished reliance on shared trips. Our findings underscore the critical need to enhance transportation integration in underserved communities. Concurrently, they highlight the importance of encouraging shared ride adoption in well-served, high-demand areas where solo ride-hailing is prevalent. We believe these results can directly inform policies that foster more equitable, cost-effective, and sustainable shared mobility systems in the post-pandemic landscape. Full article
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34 pages, 4495 KiB  
Article
Charging Ahead: Perceptions and Adoption of Electric Vehicles Among Full- and Part-Time Ridehailing Drivers in California
by Mengying Ju, Elliot Martin and Susan Shaheen
World Electr. Veh. J. 2025, 16(7), 368; https://doi.org/10.3390/wevj16070368 - 2 Jul 2025
Viewed by 752
Abstract
California’s SB 1014 (Clean Miles Standard) mandates ridehailing fleet electrification to reduce emissions from vehicle miles traveled, posing financial and infrastructure challenges for drivers. This study employs a mixed-methods approach, including expert interviews (n = 10), group discussions (n = 8), [...] Read more.
California’s SB 1014 (Clean Miles Standard) mandates ridehailing fleet electrification to reduce emissions from vehicle miles traveled, posing financial and infrastructure challenges for drivers. This study employs a mixed-methods approach, including expert interviews (n = 10), group discussions (n = 8), and a survey of full- and part-time drivers (n = 436), to examine electric vehicle (EV) adoption attitudes and policy preferences. Access to home charging and prior EV experience emerged as the most statistically significant predictors of EV acquisition. Socio-demographic variables, particularly income and age, could also influence the EV choice and sensitivity to policy design. Full-time drivers, though confident in the EV range, were concerned about income loss from the charging downtime and access to urban fast chargers. They showed a greater interest in EVs than part-time drivers and favored an income-based instant rebate at the point of sale. In contrast, part-time drivers showed greater hesitancy and were more responsive to vehicle purchase discounts (price reductions or instant rebates at the point of sale available to all customers) and charging credits (monetary incentive or prepaid allowance to offset the cost of EV charging equipment). Policymakers might target low-income full-time drivers with greater price reductions and offer charging credits (USD 500 to USD 1500) to part-time drivers needing operational and infrastructure support. Full article
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30 pages, 1818 KiB  
Article
Pooled Rideshare in the U.S.: An Exploratory Study of User Preferences
by Rakesh Gangadharaiah, Johnell Brooks, Lisa Boor, Kristin Kolodge, Haotian Su and Yunyi Jia
Vehicles 2025, 7(2), 44; https://doi.org/10.3390/vehicles7020044 - 9 May 2025
Viewed by 799
Abstract
Pooled ridesharing offers on-demand, one-way, cost-effective transportation for passengers traveling in similar directions via a shared vehicle ride with others they do not know. Despite its potential benefits, the adoption of pooled rideshare remains low in the United States. This exploratory study aims [...] Read more.
Pooled ridesharing offers on-demand, one-way, cost-effective transportation for passengers traveling in similar directions via a shared vehicle ride with others they do not know. Despite its potential benefits, the adoption of pooled rideshare remains low in the United States. This exploratory study aims to evaluate potential service improvements and features that may increase users’ willingness to adopt the service. The study analyzed transportation behaviors, rideshare preferences, and willingness to adopt pooled rideshare services among 8296 U.S. participants in 2025, building on findings from a 2021 nationwide survey of 5385 U.S. participants. The study incorporated 77 actionable items developed from the results of the 2021 survey to assess whether addressing specific user-generated topics such as safety, reliability, convenience, and privacy can improve pooled rideshare use. A side-by-side comparison of the 2021 and 2025 data revealed shifts in transportation behavior, with personal rideshare usage increasing from 22% to 28%, public transportation from 21% to 27%, and pooled rideshare from 6% to 8%, while personal vehicle (79%) use remained dominant. Participants rated features such as driver verification (94%), vehicle information (93%), peak time reliability (93%), and saving time and money (92–93%) as most important for improving rideshare services. A pre-to-post analysis of willingness to use pooled rideshare utilizing the actionable items as per respondents’ preferences showed improvement: “definitely will” increased from 15.9% to 20.1% and “probably will” rose from 35.6% to 47.7%. These results suggest that well-targeted service improvements may meaningfully enhance pooled rideshare acceptance. This study offers practical guidance for Transportation Network Companies (TNCs) and policymakers aiming to improve pooled rideshare as well as potential future research opportunities. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety)
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43 pages, 11647 KiB  
Article
The Influence of Demographic Variables on the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA)
by Rakesh Gangadharaiah, Johnell O. Brooks, Patrick J. Rosopa, Lisa Boor, Kristin Kolodge, Joseph Paul, Haotian Su and Yunyi Jia
Sustainability 2025, 17(9), 4196; https://doi.org/10.3390/su17094196 - 6 May 2025
Cited by 1 | Viewed by 399
Abstract
Building on our prior research with a national survey sample of 5385 US participants, the Pooled Rideshare Acceptance Model (PRAM) was built upon two factor analyses. This exploratory study extends the PRAM framework using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) to [...] Read more.
Building on our prior research with a national survey sample of 5385 US participants, the Pooled Rideshare Acceptance Model (PRAM) was built upon two factor analyses. This exploratory study extends the PRAM framework using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) to examine how 16 demographic variables influence and interact with the acceptance of Pooled Rideshare (PR), filling a gap in understanding user segmentation and personalization. Using a national sample of 5385 US participants, this methodological approach allowed for the evaluation of how PRAM variables such as safety, privacy, service experience, and environmental impact vary across diverse groups, including gender, generation, driver’s license, rideshare experience, education level, employment status, household size, number of children, income, vehicle ownership, and typical commuting practices. Factors such as convenience, comfort, and passenger safety did not show significant differences across the moderators, suggesting their universal importance across all demographics. Furthermore, geographical differences did not significantly impact the relationships within the model, suggesting consistent relationships across different regions. The findings highlight the need to move beyond a “one size fits all” approach, demonstrating that tailored strategies may be crucial for enhancing the adoption and satisfaction of PR services among various demographic groups. The analyses provide valuable insight for policymakers and rideshare companies looking to optimize their services and increase user engagement in PR. Full article
(This article belongs to the Special Issue Green Logistics and Intelligent Transportation)
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39 pages, 9178 KiB  
Article
Transitioning Ridehailing Fleets to Zero Emission: Economic Insights for Electric Vehicle Acquisition
by Mengying Ju, Elliot Martin and Susan Shaheen
World Electr. Veh. J. 2025, 16(3), 149; https://doi.org/10.3390/wevj16030149 - 4 Mar 2025
Cited by 2 | Viewed by 2289
Abstract
Under California’s Clean Miles Standard (or SB 1014), transportation network companies (TNCs) must transition to zero-emission vehicles by 2030. One significant hurdle for TNC drivers is the electric vehicle (EV) acquisition and operating costs versus an internal combustion engine (ICE) vehicle. This study [...] Read more.
Under California’s Clean Miles Standard (or SB 1014), transportation network companies (TNCs) must transition to zero-emission vehicles by 2030. One significant hurdle for TNC drivers is the electric vehicle (EV) acquisition and operating costs versus an internal combustion engine (ICE) vehicle. This study therefore evaluates net TNC driving earnings through EV acquisition pathways—financing, leasing, and renting—along with EV-favoring policy options. Key metrics assessed include (1) total TNC income when considering service fees, fuel costs, monthly vehicle payments, etc., and (2) the time EVs take to reach parity with their ICE counterparts. Monthly comparisons illustrate the earning differentials between new/used EVs and gas-powered vehicles. Our analyses employing TNC data from 2019 to 2020 suggest that EV leasing is optimal for short-term low-mileage drivers; EV financing is more feasible for those planning to drive for TNCs for over two years; EV rentals are only optimal for higher mileages, and they are not an economical pathway for longer-term driving. Sensitivity analyses further indicate that EV charging price discounts are effective in shortening the time for EVs to reach cost parity over ICEs. Drivers may experience a total asset gain when reselling their TNC vehicle after two to three years. Full article
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22 pages, 5015 KiB  
Article
Barriers and Benefits: Understanding Riders’ Views on Pooled Rideshare in the U.S.
by Rakesh Gangadharaiah, Johnell Brooks, Lisa Boor, Kristin Kolodge and Yunyi Jia
Vehicles 2025, 7(1), 13; https://doi.org/10.3390/vehicles7010013 - 1 Feb 2025
Cited by 1 | Viewed by 1171
Abstract
This manuscript provides actionable recommendations to enhance user satisfaction and address existing barriers regarding pooled rideshare (PR) in the United States. Despite PR’s intended benefits, such as reduced traffic congestion and cost savings, its adoption remains limited. To identify these actionable items, a [...] Read more.
This manuscript provides actionable recommendations to enhance user satisfaction and address existing barriers regarding pooled rideshare (PR) in the United States. Despite PR’s intended benefits, such as reduced traffic congestion and cost savings, its adoption remains limited. To identify these actionable items, a U.S. nationwide survey with 5385 participants explored transportation preferences, barriers, and motivators for PR use in the summer of 2021. First, two factor analyses were conducted. The first factor analysis identified the five factors associated with one’s willingness to consider PR (time/cost, traffic/environment, safety, privacy, and service experience). The second factor analysis revealed the four factors related to ways to optimize one’s PR experience (comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety). Privacy concerns, for instance, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. A structural equation model evaluated the relationships among these nine key factors influencing PR usage to develop the Pooled Rideshare Acceptance Model (PRAM). The privacy, safety, trust service, and convenience factors each had a significant large effect (Cohen’s f2 > 0.35) on the model. PRAM was extended using multigroup analyses to reveal the nuanced impact of 16 demographics, including gender, generation, rideshare experience, etc., highlighting the need for tailored strategies to improve PR acceptance through the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMAs). Multiple workshops were held with diverse audiences to translate the team’s findings to date into 84 actionable recommendations, categorized across topical areas like safety, routing, driver and passenger selection, user education, etc. These findings are a foundation for a future study to determine which items resonate with different user groups. In the meantime, the actional items serve as a user-driven resource for policymakers, transportation network companies, and researchers, offering a roadmap to potential improvements to PR services to address existing concerns with the goal of increasing the usage of PR. Full article
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39 pages, 10087 KiB  
Article
Vertiport Infrastructure Location Optimization for Equitable Access to Urban Air Mobility
by Vasileios Volakakis and Hani S. Mahmassani
Infrastructures 2024, 9(12), 239; https://doi.org/10.3390/infrastructures9120239 - 23 Dec 2024
Cited by 1 | Viewed by 2606
Abstract
Urban air mobility (UAM) has recently emerged as a promising new transportation mode, with various potential use cases. Facility location problems are well studied and of significant importance for various transportation modes. This work introduces a vertiport location identification framework, focusing on demand [...] Read more.
Urban air mobility (UAM) has recently emerged as a promising new transportation mode, with various potential use cases. Facility location problems are well studied and of significant importance for various transportation modes. This work introduces a vertiport location identification framework, focusing on demand coverage and infrastructure accessibility. An Agglomerative Hierarchical Clustering (AHC) model was utilized for the identification of candidate vertiport locations, along with a k-means algorithm, for comparison and validation purposes, based on an estimated UAM demand pattern. A genetic algorithm (GA) was then formulated, for the solution of the proposed Uncapacitated and Capacitated Vertiport Location Problems (UVLP and CVLP, respectively), variations of the Uncapacitated and Capacitated Facility Location Problems. To evaluate and compare the introduced methodology, different existing facility location problems (FLPs) were considered and solved exactly using integer and linear programming. These are the Location Set Covering Problem (LSCP), the Maximal Coverage Location Problem (MCLP), and the p-median problem. The p-center problem was also considered and solved via a heuristic approach. The proposed framework is illustrated through applications in the Chicago Metropolitan Area, with the demand estimated on the basis of existing taxi and Transportation Network Company (TNC) data. Full article
(This article belongs to the Special Issue Recent Progress in Transportation Infrastructures)
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20 pages, 3793 KiB  
Article
Travel Time Variability in Urban Mobility: Exploring Transportation System Reliability Performance Using Ridesharing Data
by Yuxin Sun and Ying Chen
Sustainability 2024, 16(18), 8103; https://doi.org/10.3390/su16188103 - 17 Sep 2024
Cited by 2 | Viewed by 2943
Abstract
Travel time variability (TTV) is a crucial indicator of transportation network performance, assessing travel time reliability and delays. This study investigates TTV metrics within the context of shared mobility using probe data from transportation network companies (TNCs) in Chicago, Los Angeles, and Dallas–Fort [...] Read more.
Travel time variability (TTV) is a crucial indicator of transportation network performance, assessing travel time reliability and delays. This study investigates TTV metrics within the context of shared mobility using probe data from transportation network companies (TNCs) in Chicago, Los Angeles, and Dallas–Fort Worth. Eight reliability metrics are analyzed and compared for each origin–destination (OD) pair in the network, including standard deviation (SD), the Planning Time Index (PTI), the Travel Time Index (TTI), the Buffer Index (BI), On-time Measures PR (alpha), and the Misery Index (MI), to evaluate their effectiveness in clustering OD pairs using K-means clustering. The findings confirm that SD, PTI, and MI are particularly effective in measuring travel time reliability and clustering within urban systems. This study identifies the most unbalanced supply–demand OD pairs/regions in each city, noting that low/medium-SD clusters around metropolitan airports indicate stable travel times even in high-demand zones, while high-SD clusters in downtown areas reveal significant traffic demands and unreliability. These patterns become more pronounced in study areas with multiple city centers. This study highlights the need for targeted strategies to enhance travel time reliability, particularly in regions like Dallas–Fort Worth, where public transportation alternatives are limited. Full article
(This article belongs to the Section Sustainable Transportation)
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32 pages, 4631 KiB  
Article
Environmental Impacts of Transportation Network Company (TNC)/Ride-Hailing Services: Evaluating Net Vehicle Miles Traveled and Greenhouse Gas Emission Impacts within San Francisco, Los Angeles, and Washington, D.C. Using Survey and Activity Data
by Elliot Martin, Susan Shaheen and Brooke Wolfe
Sustainability 2024, 16(17), 7454; https://doi.org/10.3390/su16177454 - 28 Aug 2024
Cited by 4 | Viewed by 2698
Abstract
Transportation Network Companies (TNCs) play a prominent role in mobility within cities across the globe. However, their activity has impacts on vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. This study quantifies the change in personal vehicle ownership and total miles driven [...] Read more.
Transportation Network Companies (TNCs) play a prominent role in mobility within cities across the globe. However, their activity has impacts on vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. This study quantifies the change in personal vehicle ownership and total miles driven by TNC drivers in three metropolitan areas: San Francisco, CA; Los Angeles, CA; and Washington, D.C. The data sources for this analysis comprise two surveys, one for TNC passengers (N = 8630) and one for TNC drivers (N = 5034), in addition to data provided by the TNC operators Uber and Lyft. The passenger survey was deployed within the three metropolitan areas in July and August 2016, while the driver survey was deployed from October to November 2016. The TNC operator data corresponded with these time frames and informed the distance driven by vehicles, passenger frequency of use, and fleet level fuel economies. The data from these sources were analyzed to estimate the impact of TNCs on travel behavior, personal vehicle ownership and associated VMT changes, as well as the VMT of TNCs, including app-off driving. These impacts were scaled to the population level and collectively evaluated to determine the net impacts of TNCs on VMT and GHG emissions using fuel economy factors. The results showed that the presence of TNCs led to a net increase of 234 and 242 miles per passenger per year, respectively, in Los Angeles and San Francisco, while yielding a net decrease of 83 miles per passenger per year in Washington, D.C. A sensitivity analysis evaluating net VMT change resulting from vehicle activity and key behavioral impacts revealed the conditions under which TNCs can contribute to transportation sustainability goals. Full article
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14 pages, 1760 KiB  
Article
Enhancing Demand Prediction: A Multi-Task Learning Approach for Taxis and TNCs
by Yujie Guo, Ying Chen and Yu Zhang
Sustainability 2024, 16(5), 2065; https://doi.org/10.3390/su16052065 - 1 Mar 2024
Cited by 4 | Viewed by 1954
Abstract
Taxis and Transportation Network Companies (TNCs) are important components of the urban transportation system. An accurate short-term forecast of passenger demand can help operators better allocate taxi or TNC services to achieve supply–demand balance in real time. As a result, drivers can improve [...] Read more.
Taxis and Transportation Network Companies (TNCs) are important components of the urban transportation system. An accurate short-term forecast of passenger demand can help operators better allocate taxi or TNC services to achieve supply–demand balance in real time. As a result, drivers can improve the efficiency of passenger pick-ups, thereby reducing traffic congestion and contributing to the overall sustainability of the program. Previous research has proposed sophisticated machine learning and neural-network-based models to predict the short-term demand for taxi or TNC services. However, few of them jointly consider both modes, even though the short-term demand for taxis and TNCs is closely related. By enabling information sharing between the two modes, it is possible to reduce the prediction errors for both. To improve the prediction accuracy for both modes, this study proposes a multi-task learning (MTL) model that jointly predicts the short-term demand for taxis and TNCs. The model adopts a gating mechanism that selectively shares information between the two modes to avoid negative transfer. Additionally, the model captures the second-order spatial dependency of demand by applying a graph convolutional network. To test the effectiveness of the technique, this study uses taxi and TNC demand data from Manhattan, New York, as a case study. The prediction accuracy of single-task learning and multi-task learning models are compared, and the results show that the multi-task learning approach outperforms single-task learning and benchmark models. Full article
(This article belongs to the Special Issue Sustainable Transportation and Data Science Application)
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19 pages, 5113 KiB  
Article
Spatiotemporal Heterogeneous Effects of Built Environment and Taxi Demand on Ride-Hailing Ridership
by Feiyan Zhao, Jianxiao Ma, Chaoying Yin, Wenyun Tang, Xiaoquan Wang and Jiexiang Yin
Appl. Sci. 2024, 14(1), 142; https://doi.org/10.3390/app14010142 - 22 Dec 2023
Cited by 4 | Viewed by 1815
Abstract
Researchers have applied a series of global models to investigate the link between the built environment and ride-hailing ridership based on ride-hailing data from one specific transportation network company (TNC). However, these research designs inadequately represent real ride-hailing demand within a specific spatial [...] Read more.
Researchers have applied a series of global models to investigate the link between the built environment and ride-hailing ridership based on ride-hailing data from one specific transportation network company (TNC). However, these research designs inadequately represent real ride-hailing demand within a specific spatial range and cannot reflect spatiotemporal heterogeneity in the link. For the first time, this study collects all demand data of TNCs in Nanjing and analyzes their relationship with the built environment. The effect of taxi demand is considered. We adopt a multiscale geographically weighted regression model to account for the spatial non-stationarity and the multiscale effect of each built environment variable. The findings reveal spatiotemporal heterogeneous relationships of the built environment with ride-hailing ridership. Although the relationship between taxi and ride-hailing ridership varies across spatial locations, ride-hailing always acts as a cooperator for traditional taxis. The findings provide implications for policy making, urban planning, and travel demand management of ride-hailing. Full article
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20 pages, 23510 KiB  
Article
Going My Way? Understanding Curb Management and Incentive Policies to Increase Pooling Service Use and Public Transit Linkages in the San Francisco Bay Area
by Wesley Darling, Jacquelyn Broader, Adam Cohen and Susan Shaheen
Sustainability 2023, 15(18), 13964; https://doi.org/10.3390/su151813964 - 20 Sep 2023
Cited by 1 | Viewed by 1608
Abstract
Despite lower user costs, only 20% to 40% of transportation network company (TNC) users select a pooled, or shared, ride option. Why are existing TNC users not selecting the pooled option or using TNCs to connect to public transit, and what role do [...] Read more.
Despite lower user costs, only 20% to 40% of transportation network company (TNC) users select a pooled, or shared, ride option. Why are existing TNC users not selecting the pooled option or using TNCs to connect to public transit, and what role do built environment features and incentives play in their decision? This study explores the factors that influence TNC user decisions through a multi-method approach comprising photovoice small group discussions and a workshop. Between March 2021 and May 2021, 15 San Francisco Bay Area TNC users shared photographs they took of TNC pick-up locations through two-to-three-person guided small group discussions. The photos revealed that users prefer waiting in retail or in well-lit, good-visibility locations. Participants’ primary concern was personal safety, particularly female users who may take additional precautions when walking to pick-up locations and waiting for and taking rides. In July 2021, 12 photovoice participants and 5 stakeholders provided feedback on key findings from the photography discussions. The pooling improvement strategies identified include the following: designated TNC stops with lighting and marked pick-up areas; enhanced in-app safety features; TNC partnerships with employers and retailers to incentivize riders; and mode transfer discounts for connecting TNCs to public transit. The findings suggest that safety related to the built environment plays an outsized role in a TNC user’s decision to pool or connect to public transit, and the out-of-vehicle portion of the TNC trip should be equally considered when developing policies to increase pooling. Full article
(This article belongs to the Special Issue Looking Back, Looking Ahead: Vehicle Sharing and Sustainability)
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17 pages, 1358 KiB  
Article
Modeling Choice Behaviors for Ridesplitting under a Carbon Credit Scheme
by Xiaomei Li, Yiwen Zhang, Zijie Yang, Yijun Zhu, Cihang Li and Wenxiang Li
Sustainability 2023, 15(16), 12241; https://doi.org/10.3390/su151612241 - 10 Aug 2023
Cited by 2 | Viewed by 1872
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Section Sustainable Urban and Rural Development)
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16 pages, 2649 KiB  
Article
Operational Impacts of On-Demand Ride-Pooling Service Options in Birmingham, AL
by Furat Salman, Virginia P. Sisiopiku, Jalal Khalil, Wencui Yang and Da Yan
Future Transp. 2023, 3(2), 519-534; https://doi.org/10.3390/futuretransp3020030 - 24 Apr 2023
Cited by 5 | Viewed by 2368
Abstract
Transportation Network Companies (TNCs) use online-enabled apps to provide on-demand transportation services. TNCs facilitate travelers to connect with drivers that can offer them rides for compensation using driver-owned vehicles. The ride requests can be for (a) individual or (b) shared rides. The latter, [...] Read more.
Transportation Network Companies (TNCs) use online-enabled apps to provide on-demand transportation services. TNCs facilitate travelers to connect with drivers that can offer them rides for compensation using driver-owned vehicles. The ride requests can be for (a) individual or (b) shared rides. The latter, also known as ride-pooling services, accommodates requests of unrelated parties with origins and destinations along the same route who agree to share the same vehicle, usually at a discounted fare. Uber and Lyft offer ride-pooling services in select markets. Compared to individual ride requests, ride-pooling services hold better promise toward easing urban congestion by reducing the number of automobiles on the road. However, their impact on traffic operations is still not fully understood. Using Birmingham, AL as a case study, this research evaluated the impact that ride-pooling services have on traffic operations using a Multi-Agent Transport Simulation (MATSim) model of the Birmingham metro area. Scenarios were developed to simulate baseline conditions (no TNC service) and ride-pooling availability with two types of ride-pooling services, namely door-to-door (d2d) and stop-based (sB) service and three fleet sizes (200, 400, and 800 vehicles). The results indicate that when TNC vehicles are added to the network, the Vehicle Kilometers Traveled (VKT) decrease by up to 5.78% for the door-to-door (d2d) service, and up to 2.71% for stop-based (sB) services, as compared to the baseline scenario (no TNC service). The findings also suggest that an increase in the size of the ride-pooling fleet results in a rise in total ride-pooling service VKT, network-wide total VKT, and detour distance. However, increasing the size of the ride-pooling fleet also results in a decrease in the ride request rejection rates, thus benefiting the customers and decreasing the vehicle empty ratio which, in turn, benefits the TNC drivers. The results further suggest that a fleet of 200 ride-pooling vehicles can meet the current demand for service in the Birmingham region at all times, thus it is the optimal ride-pooling TNC fleet size for a medium-sized city such as Birmingham. Full article
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28 pages, 2964 KiB  
Article
Impact of Car-Sharing and Ridesourcing on Public Transport Use: Attitudes, Preferences, and Future Intentions Regarding Sustainable Urban Mobility in the Post-Soviet City
by Rozaliia Tarnovetckaia and Hamid Mostofi
Urban Sci. 2022, 6(2), 33; https://doi.org/10.3390/urbansci6020033 - 17 May 2022
Cited by 25 | Viewed by 10394
Abstract
The impacts of ICT-based mobility services vary in different cities, depending on socioeconomic, urban form, and cultural parameters. The impacts of car-sharing and ridesourcing on public transport have not been investigated appropriately in post-Soviet Union cities. This study presents exploratory evidence on how [...] Read more.
The impacts of ICT-based mobility services vary in different cities, depending on socioeconomic, urban form, and cultural parameters. The impacts of car-sharing and ridesourcing on public transport have not been investigated appropriately in post-Soviet Union cities. This study presents exploratory evidence on how ridesourcing and car-sharing affect public transport usage in Moscow. Additionally, it studies how demographics, spatial parameters, attitudes, and travel preferences influence the frequency of use of ridesourcing and car-sharing in Moscow. An online mobility survey was conducted at the beginning of 2020 among respondents (sample size is 777) in the Moscow agglomeration. Overall, 66% of ridesourcing users shifted from public transport to these mobility services, which shows the substitutional impact of ridesourcing on public transport. Additionally, the logit model indicates that the regular use of ridesourcing negatively correlates with the regular use of buses/trams/trolleybuses in Moscow. The impact of car-sharing on public transport seems less substitutional and more complementary than the impact of ridesourcing. Overall, 40% of car-sharing users would replace their last car-sharing trip with public transport if car-sharing was unavailable. Moreover, the logit model indicates a positive association between the regular use of car-sharing and the use of buses/trams/trolleybuses. Moreover, the modal split analysis shows a bigger share of public transport use and walking than car use among citizens’ urban journeys in Moscow. Full article
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