An Analysis on Excursion Characteristics of Electric Assist Bicycles by Travel Behavioral Comparison Based on Trajectory Data

Inagaki, Tomoyuki; Mimura, Yasuhiro; Ando, Ryosuke

doi:10.2208/jscejipm.67.67_i_683

Cited by 3 publications

(7 citation statements)

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Section: Travel Time Settingsmentioning

confidence: 79%

“…For every resulting cycling network link, speed is assigned in relation to gradient according to empirical data from Inagaki et al (2011). Note that in this study, e-biking speeds are shown to be lower than bicycling speeds for uphill segments, whereas Inagaki et al (2011) stated that there is no significant difference between e-biking and conventional bicycling in all three scenarios; they speculated that the difference may result from the feature of e-bikes and that the assist ratio drops when the speed increases. This set of data was adopted since it was the only data that could be found regarding the measured value of the gradientspeed relationship for e-biking uphill and downhill in Japan, and the number of samples is relatively large (294 bicycle riders and 5854 road links).…”

Section: Travel Time Settingsmentioning

confidence: 79%

“…1.) was obtained from existing studies (Ainsworth et al, 2011;Hagiwara and Yamamoto, 2011;Inagaki et al, 2011;National Institute of Health and Nutrition, 2012). While there are empirical studies on physical activity in cycling and e-biking (Table 3.…”

Section: Travel Time Settingsmentioning

confidence: 99%

“…where Cv is the speed of the bicycle (m/s), which is set based on measured bicycling speed data on roads with different gradients (Inagaki et al, 2011); s is the road gradient in percentage, set to -7-7 as an integer; and M is the gross mass (kg), including a 15-kg bicycle and a 60-kg cyclist. In terms of the other factors, we assumed the mechanical efficiency of the bicycle ( mech ) to be 95%, gravitational acceleration (g) to be 9.807 m/s 2 , rolling resistance coefficient (Cr) to be 0.008, aerodynamic drag coefficient (CD) to be 1.2, the frontal area of the cyclist and bicycle (A) to be 0.616 m 2 , the density of air ( ) to be 1.226 kg/m 3 , and the headwind ( w ) to be 0 m/s.…”

Section: Travel Time Settingsmentioning

confidence: 99%

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Quantifying e-bike applicability by comparing travel time and physical energy expenditure: A case study of Japanese cities

Liu

Suzuki

2019

Journal of Transport & Health

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Background E-bikes are a growing market around the world and public policies regarding their usage are varied among cities. There is a need to inform policy decisions about integrating e-bikes into urban transportation systems. While previous studies on bicycle convenience focused on the cycling environment itself, assessment of a new transport mode, like e-bikes, requires insights into their potential and limitations when introduced into the existing urban transportation system. Methods E-bike applicability is defined as the change of convenience due to their introduction and the service area is adopted as a measure of convenience for a transportation mode. Indices for e-bike applicability are proposed by comparing the service area of e-bikes to public transit and to conventional bicycles considering travel time and physical energy expenditure as two measures. The methods are applied to four Japanese cities to assess applicability on two scales, namely a community-wide scale and a city-wide scale. ResultsOn the community-wide scale, e-bikes are applicable to areas with steep road gradients, areas with geographical obstacles requiring detours, and areas lacking public transportation. E-bike applicable communities with high likely e-bike demand are selected. On the city-wide scale, e-bikes are applicable to short distance trips in cities with well-developed transit systems, with applicable travel time and physical energy expenditure range of 65 min and 1.25 MET-h round trip, respectively. E-bikes are a promising alternative means of transport in local cities; they also have limitation in terms of physical energy expenditure compared to transit. ConclusionsThe indices can be valuable tools providing urban planners with knowledge about e-bikes on a communitywide scale and a city-wide scale. Keywords E-bike, Bicycle, Travel time, Physical energy expenditure Research objectivesThis research aims to evaluate e-bike convenience for local users when introduced into the existing urban transportation system to inform policy decisions for integrating e-bikes into urban transportation systems. While previous studies on bicycle convenience focused on the cycling environment itself, assessment of a new transport mode like e-bikes requires insights into their potential and limitations, which can be determined by comparing it to existing transportation modes. Therefore, we define e-bike applicability as the change of convenience due to the introduction of e-bikes into the existing urban transportation system, and we propose an assessment methodology based on the comparisons.In this research, e-bikes refer to the electric-power-assisted bicycles that are legally defined as bicycles according to Japan law (Road Traffic Act, 2015). They use motors to supplement human power, and the assist rate to human force is a maximum of two when the speed is less than 10 km/h, and this rate gradually decreases as the speed increases and becomes 0 at 24 km/h (Regulation for Enforcement of the Road Traffic Act, 2018). The two alternative transpor...

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Section: Travel Time Settingsmentioning

confidence: 79%

Section: Travel Time Settingsmentioning

confidence: 79%

Section: Travel Time Settingsmentioning

confidence: 99%

Section: Travel Time Settingsmentioning

confidence: 99%

See 2 more Smart Citations

Quantifying e-bike applicability by comparing travel time and physical energy expenditure: A case study of Japanese cities

Liu

Suzuki

2019

Journal of Transport & Health

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“…Moreover, shared e-bikes attract additional user groups who carry loads when traveling [42] or suffer from physical defects, which do not allow bicycle pedaling [43]. Fewer existing studies are related to shared e-bikes, most of which were based on survey data to study the performance of e-bikes [44], users' mobility behavior [45][46][47], and travel mode influencing factors [48]. Li and Dai [49] completed data cleaning of shared e-bike trajectories based on the speed and time interval rules for the first time, without considering the trajectory semantic information.…”

Section: Experimental Datamentioning

confidence: 99%

Trip Extraction of Shared Electric Bikes Based on Multi-Rule-Constrained Homomorphic Linear Clustering Algorithm

Cheng

et al. 2019

IJGI

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Trajectory data include rich interactive information of humans. The correct identification of trips is the key to trajectory data mining and its application. A new method, multi-rule-constrained homomorphic linear clustering (MCHLC), is proposed to extract trips from raw trajectory data. From the perspective of the workflow, the MCHLC algorithm consists of three parts. The first part is to form the original sub-trajectory moving/stopping clusters, which are obtained by sequentially clustering trajectory elements of the same motion status. The second part is to determine and revise the motion status of the original sub-trajectory clusters by the speed, time duration, directional constraint, and contextual constraint to construct the stop/move model. The third part is to extract users’ trips by filtering the stop/move model using the following rules: distance rule, average speed rule, shortest path rule, and completeness rule, which are related to daily riding experiences. Verification of the new method is carried out with the shared electric bike trajectory data of one week in Tengzhou city, evaluated by three indexes (precision, recall, and F1-score). The experiment shows that the index values of the new algorithm are higher (above 93%) than those of the baseline methods, indicating that the new algorithm is better. Compared to the baseline velocity sequence linear clustering (VSLC) algorithm, the performance of the new algorithm is improved by approximately 10%, mainly owing to two factors, directional constraint and contextual constraint. The better experimental results indicate that the new algorithm is suitable to extract trips from the sparse trajectories of shared e-bikes and other transportation forms, which can provide technical support for urban hotspot detection and hot route identification.

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Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data

Cheng

et al. 2020

IJGI

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Human activities generate diverse and sophisticated functional areas and may impact the existing planning of functional areas. Understanding the relationship between human activities and functional areas is key to identifying the real-time urban functional areas based on trajectories. Few previous studies have analyzed the interactive information on humans and regions for functional area identification. The relationship between human activities and residential areas is the most representative for urban functional areas because residential areas cover a wide range and are closely connected with human life. The aim of this paper is to propose the CARA (Commuting Activity and Residential Area) model to quantify the correlation between human activities and urban residential areas. In this model, human activities are represented by hot spots extracted by the Gaussian Mixture Model algorithm while residential areas are represented by POI (point of interest) data. The model shows that human activities and residential areas present a logarithmic relationship. The CARA model is further assessed by retrieving urban residential areas in Tengzhou City from shared e-bike trajectories. Compared with the actual map, the accuracy reaches 83.3%, thus demonstrating the model’s reliability and feasibility. This study provides a new method for functional areas identification based on trajectory data, which is helpful for formulating the urban people-oriented policies.

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An Analysis on Excursion Characteristics of Electric Assist Bicycles by Travel Behavioral Comparison Based on Trajectory Data

Cited by 3 publications

References 0 publications

Quantifying e-bike applicability by comparing travel time and physical energy expenditure: A case study of Japanese cities

Quantifying e-bike applicability by comparing travel time and physical energy expenditure: A case study of Japanese cities

Trip Extraction of Shared Electric Bikes Based on Multi-Rule-Constrained Homomorphic Linear Clustering Algorithm

Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data

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