GPS data in urban online ride-hailing: A comparative analysis on fuel consumption and emissions

Sui, Yi; Qiu, Rui; Song, Xuan; Shao, Fengjing; Yu, Xiang; Shibasaki, Ryosuke; Sun, Rechengcheng; Yuan, Meng; Wang, Changying; Li, Shujing; Li, Yao

doi:10.1016/j.jclepro.2019.04.159

Cited by 94 publications

(37 citation statements)

References 31 publications

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“…Considering the real driving condition of buses, unreasonable records of buses are filtered out, such as those average travel speed more than 40 km/h (transit buses speed limit in Qingdao), a sudden distance deviation over 100 m (GPS error), and long idle condition more than 30 min for buses (probably caused by road accident). Unreasonable trips of taxis are also removed as shown in our previous study [28] . After these pre-processing steps, there are 2056 buses with an average 1.5 million passengers’ trips data and 7589 taxis with 0.2 million passengers’ trips data each day.…”

Section: Study Area and Datamentioning

confidence: 99%

Mining urban sustainable performance: Spatio-temporal emission potential changes of urban transit buses in post-COVID-19 future

et al. 2020

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Highlights Proposing a framework to analyze emission patterns of buses and changes in post-COVID-19. 2056 buses with 1.5 million ridership and 7589 taxis with 0.2 million trips are used for analysis. 224 social surveys are collected and show a 56.3% ridership reduction in post-COVID-19. We find buses cannot be “greener” travel modal than cars if ridership reduces by more than 40%.

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Section: Study Area and Datamentioning

confidence: 99%

Mining urban sustainable performance: Spatio-temporal emission potential changes of urban transit buses in post-COVID-19 future

et al. 2020

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“…Online car-hailing refers to the business activities of building a service platform based on Internet technology and providing non-cruise-booking taxi services, which brings many benefits, such as reducing the use of private cars [1,2], easing traffic congestion, improving the environment [3,4]. As a new business model, online car-hailing first emerged in Europe and America, and has been increasing in popularity since entering China in 2012 [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the influencing factors of the unsafe driving behaviors of online car-hailing drivers in china

Xiao

2020

PLoS ONE

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Online car-hailing drivers are a special group between professional drivers and private car drivers. The paper built the unsafe driving behavior model of online car-hailing drivers based on the theory of planned behavior (TPB), explored the socio-psychological factors underlying drivers' motivation for unsafe driving behavior and examined how these factors predict their behaviors. 239 online car-hailing drivers were surveyed with a self-reported questionnaire. Factors analysis proved the TPB questionnaire to be valid and reliable. Structural equation modeling showed that attitude to behavior (0.18), subjective norm(0.39) significantly influenced drivers' behavioral intention, perceived behavioral control (0.27) could both affected drivers' behavioral intention (0.27) and behavior(0.21),behavioral intention was the most direct and important predictor of behavior. This study provided a valuable contribution to designing more effective interventions to improve driving safety of online car-hailing drivers.

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Section: Mining and Fusion Of Online Ride-hailing Trip Datamentioning

confidence: 99%

“…At present, various researchers [38][39][40][41] applied these data to model research for different fields. Compared with taxi trajectory data, Sui et al [38] found that online ride-hailing has a lower empty-load rate and less detour behavior, which can provide better trip services. Wang et al [39] analyzed residents' hospitalization through this database, which contributed to the decision-making of infrastructure Energies 2020, 13,1412 5 of 32 configuration for institutions, such as urban planning departments and hospitals.…”

Section: Mining and Fusion Of Online Ride-hailing Trip Datamentioning

confidence: 99%

Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

et al. 2020

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Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the 'Electric Vehicles-Power Grid-Traffic Network' fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners' charging behavior.Energies 2020, 13, 1412 2 of 32 331,000 public charging piles and 477,000 private charging piles in 2018, an increase of 74.2% over the same period in 2017 [6].However, the driving and charging behaviors of numerous EVs in urban internal networks are bound to interact with the energy and information generated by the traffic network and power grid [7,8]. The residents' trip rule, urban road network structure and charging facility distribution affect the vehicle driving distribution and charging selection, whereas the vehicle battery parameters, driving path plan and human decision-making behavior also affect the degree of traffic network obstruction and the power grid operation state [9][10][11][12]. Therefore, accurate prediction of EV charging demand and reasonable fast-charging station (FCS) recommendations are the premise of realizing compatibility between EVs and the power grid along with the transportation network.At present, various studies have developed EV charging demand models from the aspect of cooperation between EVs, the transportation system and power system all together. Hence, in [13,14], an origin destination (OD) matrix analysis method was utilized to track the all-weather driving trajectory of EVs and to predict the charging load distribution of the regional electricity grid as well as the flow status of road networks through vehicle traffic trip demands. Several studies [15][16][17] introduced Traffic Trip Chain and Markov Decision Chain to simulate the dynamic driving behavior and random charging behavior o...

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GPS data in urban online ride-hailing: A comparative analysis on fuel consumption and emissions

Cited by 94 publications

References 31 publications

Mining urban sustainable performance: Spatio-temporal emission potential changes of urban transit buses in post-COVID-19 future

Mining urban sustainable performance: Spatio-temporal emission potential changes of urban transit buses in post-COVID-19 future

Analysis of the influencing factors of the unsafe driving behaviors of online car-hailing drivers in china

Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

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