scholarly journals A Contrastive Study on Travel Costs of Car-Sharing and Taxis Based on GPS Trajectory Data

2020 ◽  
Vol 17 (24) ◽  
pp. 9446
Author(s):  
Beibei Hu ◽  
Yue Sun ◽  
Huijun Sun ◽  
Xianlei Dong
Keyword(s):  
Market Competition ◽  
Travel Cost ◽  
Cost Calculation ◽  
Cost Advantage ◽  
Trajectory Data ◽  
Car Sharing ◽  
Travel Costs ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
The Cost

The emergence and development of car-sharing has not only satisfied people’s diverse travel needs, but also brought new solutions for improving urban traffic conditions and achieving low-carbon and green sustainable development. In recent years, car-sharing has had competition with other ways of getting around, as the acceptance of car-sharing has grown, notably taxis. Therefore, it is particularly important to explore car-sharing travel costs advantages from the perspective of consumers and discover the competitive and complementary spaces between car-sharing and other modes. Therefore, taking Beijing as an example, this paper uses GPS trajectory data based on car-sharing orders to design a travel cost framework of car-sharing and taxis. We calculate and compare the travel cost difference between these two modes under different travel characteristics. The results indicate that car-sharing is a more economical way for consumers to travel for short or medium lengths of time, while people are more inclined to take taxis for distances of long duration. Compared with on workdays, at the weekend, the cost advantage of car-sharing is greater for long-distance trips. Moreover, the cost advantage of car-sharing increases gradually with the increase in travel distance. In addition, the travel costs of car-sharing and taxis are also affected by peak and off-peak traffic periods. Compared with off-peak periods, it is more cost-effective for travelers to take taxis during peak traffic periods for various travel distances. From the perspective of the travel cost, it is of great theoretical significance to discuss the substitution (market competition) and complementary relationship (market cooperation) between car-sharing and taxis in a detailed and systematic way. It provides methods and ideas for the comparative cost calculation of car-sharing and other travel modes. This paper also provides enlightenment and guidance for the development of car-sharing. Enterprises should implement differentiated pricing, designing different charging methods for different traffic periods, travel miles, and rental times, and set up additional stations in the surrounding areas of the city. Relevant government departments should also strictly manage the market access of car-sharing, and add or open car-sharing parking lots in centralized areas and for specific periods.

scholarly journals Understanding the Competitive Advantages of Car Sharing from the Travel-Cost Perspective

2020 ◽  
Vol 17 (13) ◽  
pp. 4666 ◽  
Author(s):  
Xianlei Dong ◽  
Yongfang Cai ◽  
Jiaming Cheng ◽  
Beibei Hu ◽  
Huijun Sun
Keyword(s):  
Urban Areas ◽  
Market Competition ◽  
Cost Effective ◽  
Travel Cost ◽  
Research Area ◽  
Cost Advantage ◽  
Market Demand ◽  
Low Carbon ◽  
Trajectory Data ◽  
Car Sharing

The emergence and development of car sharing can not only satisfy people’s diverse travel demands, but also can bring a new solution to facilitate urban low-carbon and green development. With the increasing acceptance of car sharing, the market competition between car sharing and traditional taxis is becoming increasingly fierce. Therefore, we explore the advantages of car sharing to travelers compared with taxis. In this paper, we first use the GPS (Global Positioning System) trajectory data of car sharing orders to construct a comparative advantage model based on travel-cost. Then, we take Beijing as the research area to explore the travel-cost advantages of car sharing in terms of the time and space dimensions compared with taxis, through calculating the travel-cost of car sharing and using simulation to calculate that of taxis. The results of the comparison between car sharing and taxis from the perspective of travel-cost are as follows: (1) Compared with short trips, the travel-cost advantage of car sharing is relatively higher in medium and long trips; for travelers, the taxi has a higher travel-cost advantage when the travel time is either very long or very short. (2) On weekdays, it is more cost-effective to travel by shared cars for travelers before the rush hours in the evening, and the travel-cost advantage of using taxis is greater after the evening peak. (3) Compared with weekdays, it is more cost-effective to travel by shared cars on weekends wherever travelers are living in the main urban areas or in the remote suburbs. It is suggested that relevant departments should understand the travelers’ preference and analyze the influence mechanism of other various factors on the market demand for car sharing as per the focus on the market on the travel-cost advantages of car sharing, so as to promote the healthy and sustainable development of urban shared transportation.

A Heading Maintaining Oriented Compression Algorithm for GPS Trajectory Data

Informatica ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 33-52 ◽  
Author(s):  
Pengfei HAO ◽  
Chunlong YAO ◽  
Qingbin MENG ◽  
Xiaoqiang YU ◽  
Xu LI
Keyword(s):  
Compression Algorithm ◽  
Trajectory Data ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Enabling Smart Urban Services with GPS Trajectory Data

2021 ◽  
Author(s):  
Chao Chen ◽  
Daqing Zhang ◽  
Yasha Wang ◽  
Hongyu Huang
Keyword(s):  
Trajectory Data ◽  
Urban Services ◽  
Gps Trajectory Data ◽  
Gps Trajectory

scholarly journals An Automatic Method for Detection and Update of Additive Changes in Road Network with GPS Trajectory Data

2019 ◽  
Vol 8 (9) ◽  
pp. 411 ◽  
Author(s):  
Tang ◽  
Deng ◽  
Huang ◽  
Liu ◽  
Chen
Keyword(s):  
Road Network ◽  
Sampling Rate ◽  
Quality Data ◽  
Automatic Method ◽  
Trajectory Data ◽  
The Road ◽  
High Sampling Rate ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently.

scholarly journals Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data

2020 ◽  
Vol 9 (3) ◽  
pp. 181
Author(s):  
Banqiao Chen ◽  
Chibiao Ding ◽  
Wenjuan Ren ◽  
Guangluan Xu
Keyword(s):  
Clustering Algorithm ◽  
Recall Rate ◽  
Location Based Services ◽  
Trajectory Data ◽  
Intersection Detection ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Road Intersections ◽  
Road Maps ◽  
Gps Trajectory

The requirements of location-based services have generated an increasing need for up-to-date digital road maps. However, traditional methods are expensive and time-consuming, requiring many skilled operators. The feasibility of using massive GPS trajectory data provides a cheap and quick means for generating and updating road maps. The detection of road intersections, being the critical component of a road map, is a key problem in map generation. Unfortunately, low sampling rates and high disparities are ubiquitous among floating car data (FCD), making road intersection detection from such GPS trajectories very challenging. In this paper, we extend a point clustering-based road intersection detection framework to include a post-classification course, which utilizes the geometric features of road intersections. First, we propose a novel turn-point position compensation algorithm, in order to improve the concentration of selected turn-points under low sampling rates. The initial detection results given by the clustering algorithm are recall-focused. Then, we rule out false detections in an extended classification course based on an image thinning algorithm. The detection results of the proposed method are quantitatively evaluated by matching with intersections from OpenStreetMap using a variety of distance thresholds. Compared with other methods, our approach can achieve a much higher recall rate and better overall performance, thereby better supporting map generation and other similar applications.

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