Analysing driving patterns of electric taxi based on the location of charging station in urban area

Ahn, Yongjun; Chulmin, Jun; Yeo, Hwasoo

doi:10.1109/isc2.2016.7580836

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…Similar assumption is also observed when exploring EV feasibility problems [39] and also see its application in the charging facility planning problems [37,38]. There are other studies assuming that ET drivers tend to charge at their own stations (home location) [40,41], which further diversifies the possible outcomes regarding the driver location preference and charging decision-making.…”

Section: Drivers' Choice For Time Of Chargingsupporting

confidence: 58%

“…Major assumption Source Problem Competing behavior among EV users Route choice and charging time equilibrium [16], [29], [18], [30], [31], [32], [17], [33], [34], [35], [36] Charging facility planning Drivers' preference for charging station location Always go to the nearest charging station [37], [38], [19], [8], [39] ET charging schedule and dispatching strategy; EV feasibility Drivers tend to charge at preferred charging station [40], [41] ET charging schedule…”

Section: Charging Behaviormentioning

confidence: 99%

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Understanding charging dynamics of fully-electrified taxi services using large-scale trajectory data

Liu¹,

Guo²,

Qian³

et al. 2021

Preprint

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An accurate understanding of "when, where and why" of charging activities is crucial for the optimal planning and operation of E-shared mobility services. In this study, we leverage a unique trajectory of a city-wide fully electrified taxi fleet in Shenzhen, China, and we present one of the first studies to investigate charging behavioral dynamics of a fully electrified shared mobility system from both system-level and individual driver perspectives. The electric taxi (ET) trajectory data contain detailed travel information of over 20,000 ETs over one month period. By combing the trajectory and charging infrastructure data, we reveal remarkable regularities in infrastructure utilization, temporal and spatial charging dynamics as well as individual driver level charging preferences. Specifically, we report that both temporal and spatial distributions of system-level charging activities present strong within-day and daily regularities, and most charging activities are induced from drivers' shift schedules. Further, with 425 charging stations, we observe that the drivers show strong preferences over a small subset of charging stations, and the power-law distribution can well characterize the charging frequency at each charging station. Finally, we show that drivers' shift schedules also dominate the individual charging behavior, and there are strikingly stable daily charging patterns at the individual level. The results and findings of our study represent lessons and insights that may be carried over to the planning and operation of E-shared mobility in other cities and deliver important justifications for future studies on the modeling of E-shared mobility services.

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Section: Drivers' Choice For Time Of Chargingsupporting

confidence: 58%

Section: Charging Behaviormentioning

confidence: 99%

Understanding charging dynamics of fully-electrified taxi services using large-scale trajectory data

Liu¹,

Guo²,

Qian³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Secondly, an electric vehicle public charging station coverage positioning model is proposed to maximize the existing activities of electric vehicle drivers. Based on real trajectory data, Ahn et al [27] analyzed the driving patterns of electric taxis under different constraints. Luo et al [28] studied the interaction between travel patterns, EV driver behavior, urban road network, power grid network, and charging station layout, and proposed a multi-stage charging station layout method with different EV penetration rates.…”

Section: Related Workmentioning

confidence: 99%

A Data-Driven Approach for Optimizing the EV Charging Stations Network

Zhang

Meng

2020

IEEE Access

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With the exhaustion of oil resources and the aggravation of environmental pollution, electric vehicles, as the main force of new energy consumption, have a more and more promising development prospect. In China, the utilization rate of charging facilities in the public is very low, and there is a large number of redundant charging stations that waste resources. Charging station congestion, meanwhile, is one of the reasons why it is difficult to charge for electric vehicles. This paper proposes a data-driven approach to optimize the existing charging station network by eliminating redundant charging stations, and to identify the charging station congestion areas in the original charging network to provide suggestions for further solving the difficulty of charging electric vehicles. Firstly, we infer that the fine-grained charging situation (consisting of the waiting time and the visiting rate) at different stations. Using a 3D tensor, we model the charging behavior of the electric vehicle, in which the three dimensions represent stations, hours, and days respectively. Secondly, for times and stations with sparse data, we use a context-aware tensor collaborative decomposition method to estimate the situation. For charging stations in a specific period of time, we separately set up a queue system for them to estimate their visiting rate and detect the distribution characteristics of EV charging hotspots in the city. Finally, we introduce a flexible scoring function to evaluate the usage benefits of charging stations and propose a heuristic network expansion algorithm to optimize the network. Applying the data-driven approach to Wuhan city, the results show that using our method can eliminate redundant sites while increasing utilization and find charging station congestion area to guide the government to further charging station planning. Our approach can be adapted for other optimal problems such as chain supermarket layout, public facility planning, and resources configuration using trajectory data.

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“…The review categorized 661 studies based on their structure (user, destination, or route orientation), overarching objectives (like demand density, trip length, or queuing), and data sources (statistics, measured travel data, simulations or surveys) and discussed the advantages and limitations of the applied data and methods. Some studies, for instance, Ahn and Yeo [23], Tu et al [15], and Wagner et al [18], are based on real data from existing CSs or travel data from tracked EVs. However, the number of EV fleets is still at a low level [1], making the validity of results and overall transferability possibly uncertain.…”

Section: State Of the Art Of Cs Location Modelsmentioning

confidence: 99%

Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations

2019

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In recent years, with the increased focus on climate protection, electric vehicles (EVs) have become a relevant alternative to conventional motorized vehicles. Even though the market share of EVs is still comparatively low, there are ongoing considerations for integrating EVs in transportation systems. Along with pushing EV sales numbers, the installation of charging infrastructure is necessary. This paper presents a user- and destination-based approach for locating charging stations (CSs) for EVs—the electric charging demand location (ECDL) model. With regard to the daily activities of potential EV users, potential positions for CSs are derived on a micro-location level in public and semipublic spaces using geographic information systems (GIS). Depending on the vehicle users’ dwell times and visiting frequencies at potential points of interest (POIs), the charging demand at such locations is calculated. The model is mainly based on a survey analyzing the average time spent per daily activity, regional data about driver and vehicle ownership numbers, and the georeferenced localization of regularly visited POIs. Optimal sites for parking and charging EVs within the POIs neighborhood are selected based on walking distance calculations, including spatial neighborhood effects, such as the density of POIs. In a case study in southeastern Germany, the model identifies concrete places with the highest overall demand for CSs, resulting in an extensive coverage of the electric energy demand while considering as many destinations within the acceptable walking distance threshold as possible.

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Analysing driving patterns of electric taxi based on the location of charging station in urban area

Cited by 9 publications

References 6 publications

Understanding charging dynamics of fully-electrified taxi services using large-scale trajectory data

Understanding charging dynamics of fully-electrified taxi services using large-scale trajectory data

A Data-Driven Approach for Optimizing the EV Charging Stations Network

Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations

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