Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data

Sun, Daniel; Kaisheng, Zhang; Shen, Suwan

doi:10.1016/j.trd.2018.04.024

Cited by 141 publications

(78 citation statements)

References 37 publications

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“…Compared with other clustering algorithms [36], DBSCAN possesses superior performance, which is insensitive to the order of the points in the dataset and does not require the number of the clusters a priori. Two important parameters for the algorithm include the number of occurrences and the radius: MinPts and Dist, which were chosen as 10 and 200 m in this study.…”

Section: Classification Of the Accessible Charging Locations Of Each mentioning

confidence: 99%

A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

Yun

Sun

Zhang³

et al. 2019

Sustainability

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Electric vehicles (EVs) are promising alternatives to replace traditional gasoline vehicles. The relationship between available charging stations and electric vehicles has to be precisely coordinated to facilitate the increasing promotion and usage of EVs. This paper aims to investigate the choice of the charging location with global positioning system (GPS) trajectories of 700 Plug-in Hybrid Electric Vehicle (PHEV) users as well as the charging facility data in Shanghai. First, the recharge accessibility of each PHEV user was investigated, and 9% rely solely on public charging networks. Then, we explored the relationship between fuel consumption and the average distance between charging to analyze the environmental benefits of PHEVs. It was found that 16% PHEVs are similar to EVs, and 9% whose drivers rely solely on public charging stations are similar to internal combustion engine (ICE) vehicles. PHEV users were divided into four types based on the actual recharge access: home and workplace-based user (private + workplace + public), the home-based user (private + public), the workplace-based user (workplace + public), and the public-based user (public). Models were developed to identify and compare the factors that influence PHEV user's charging location choices (home, workplace, and public stations). The modeling and results interpretation were carried out for all PHEV users, home and workplace-based users, home-based users, and workplace-based users, respectively. The estimation results demonstrated that PHEV users tended to charge at home or workplace rather than public charging stations. Charging price, charging price tariff, the initial state of charge (SOC), dwell time, charging power, the density and size of public charging stations, the total number of public charging, vehicle kilometer travel (VKT) of the current trip and current day are the main predictors when choosing the charging location. Findings of this study may provide new insights into the operational strategies of the public charging station as well as the deployment of public charging facilities in urban cities.Sustainability 2019, 11, 5761 2 of 23 electrification through considerable investments in consumer incentives, charging infrastructures as well as technology research and development [4,5]. In order to promote the usage and adoption of EVs, Shanghai had made great investments to subsidize EV consumers and manufacturers, offered free and specific license plate, built public charging stations, and offered purchasing tax deductions and exemptions and other non-monetary incentives (e.g., free usage restriction policy during peak hours) [6]. In 2016, the EV ownership in Shanghai has surpassed the 100,000, ranking the first in the world. By 2018, the number had increased to attain 239,000, accounting for 6% of the total vehicle ownership in Shanghai [7], in which percentages of BEV and PHEV were around 30% and 70%, respectively.One of the most pressing challenges is to deploy charging infrastructures in public locations considering the tra...

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Section: Classification Of the Accessible Charging Locations Of Each mentioning

confidence: 99%

A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

Yun

Sun

Zhang³

et al. 2019

Sustainability

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“…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.…”

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

confidence: 99%

“…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. Sun et al [41] analyzed spatio-temporal traffic line source emissions based on massive online car-hailing service data, which provided support for traffic network construction planning.…”

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

confidence: 99%

“…Energies 2020, 13, x FOR PEER REVIEW 5 of 32 et al [41] analyzed spatio-temporal traffic line source emissions based on massive online car-hailing service data, which provided support for traffic network construction planning. Herein, we continue to apply online ride-hailing trip data [37] into the study of urban EV fastcharging demand modeling.…”

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

confidence: 99%

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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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“…One of the main tasks was to investigate network division, thus obtaining a well-defined MFD. The most classic method was developed by Ji and Geroliminis [19] that divided the entire network according to the congestion feature [20,21], and then the dynamic division problem was also studied. Keyvan-Ekbatani et al [22] studied the feedback gate control method using the simulation network with perimeter gate control and obtained satisfying results with lower total travel time.…”

Section: Introductionmentioning

confidence: 99%

A Two-Layer Network Dynamic Congestion Pricing Based on Macroscopic Fundamental Diagram

Wei

Sun

2018

Journal of Advanced Transportation

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Dynamic congestion pricing has attracted increasing attentions during the recent years. Nevertheless, limited research has been conducted to address the dynamic tolling scheme at the network level, such as to cooperatively manage two alternative networks with heterogeneous properties, e.g., the two-layer network consisting of both expressway and arterial network in the urban areas. Recently, the macroscopic fundamental diagram (MFD) developed by both field experiments and simulation tests illustrates a unimodal low-scatter relationship between the mean flow and density network widely, providing the network traffic state is roughly homogeneous. It reveals traffic flow properties at an aggregated level and sheds light on dynamic traffic management of a large network. This paper proposes a bilevel programming toll model, incorporating MFD to solve the unbalanced flow distribution problem within the two-layer transportation networks. The upper level model aims at minimizing the total travel time, while the lower level focuses on the MFD-based traffic assignment, which extends the link-based traffic assignment to network wide level. Genetic algorithm (GA) and the method of successive average were adopted for solving the proposed model, on which an online experimental platform was established using VISSIM, MATLAB, and Visual Studio software packages. The results of numerical studies demonstrate that the total travel time is decreased by imposing the dynamic toll, while the total travel time savings significantly outweigh the toll paid. Consequently, the proposed dynamic toll scheme is believed to be effective from both traffic and economic points of view.

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Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data

Cited by 141 publications

References 37 publications

A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users

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

A Two-Layer Network Dynamic Congestion Pricing Based on Macroscopic Fundamental Diagram

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