Urban taxis and air pollution a case study in harbin china

For the past few years, cities in northern India have been covered in a thick layer of winter smog. The situation has turned quite drastic in the National Capital, Delhi.

Urban taxis and air pollution a case study in harbin china

This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Introduction As an important component of the urban public passenger transportation system, taxi offers an all-weather, convenient, comfortable, and personalized travel services for the urban residents, as well as playing a key role in the urban passenger transportation development [ 1 ].

Taking the capital of China, Beijing, as an example, there were 66, registered taxi vehicles in the yearand these taxis carried million passengers annually which means more than 2 million passengers per dayaccounting for 9.

We assume that there is relationship between the two activity spaces, drop-off locations and pick-up locations.

A major application of activity theory is empirical measurement and analysis of space time activity STA data or records of where and when individuals conducted activities over a daily, weekly, or monthly cycle. Wang and Cheng [ 15 ] had summarized the basic components in the activity theory, which includes the activity, activity frequency, activity destination, trip, transport mode sand the activity space.

The pioneer work can be traced to Giraudo and Peruch [ 19 ], Peruch et al. Besides the traditional comparison of the cognitive maps method, Wakabayashi et al. This research can also be helpful for saving taxi energy consumption and lowering air pollution emissions, to achieve a more sustainable and environmental development of urban taxi industry.

The paper is organized as follows. Section 4 describes and compares the analysis results. Section 5 is the discussions. Finally, we conclude this paper in Section 6. In the research of Ge et al. Susilo and Kitamura [ 16 ] had extended the action space to the second moment of the activity locations that it contains, and then they examined the day-to-day variation in the second moment.

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Based on existing researches, we divided these measurements into two categories, the spatial distribution category and the extended second moments of activity locations measurement category.

The Spatial Distribution Category The spatial distribution estimates the basic parameters about the distribution; they include mean centre, standard deviation of the and coordinates, standard deviational ellipse, standard distance deviation, and convex hull.

The mean centre MC is the average location of taxi service events in the space including the pick-up and drop-off eventwhich can be calculated by [ 28 ] whereare the coordinates of the mean centre, which can determine the space location of the MC,are the coordinates of taxi service event in the two-dimensional, respectively, and is the total number of taxi service events.

Standard distance deviation SDD [ 2829 ] can describe the absolute dispersion degree for each taxi service event relative to the mean centre MC ; the formula can be expressed by where are the coordinates of taxi service event andare the coordinates of the mean centre.

Based on the mean centre and standard distance deviation, we can draw the standard deviational circle SDCwhich can express the dispersion of taxi service in all directions of space.

The detailed formulas are as follows: Figure 1 has shown a taxi operation example in Shenzhen from 0 am on April 18, to 12 am on April 26, which consists of continuous hours in Shenzhen, China.

The red points represent the taxi drop-off activity locations, while the cyan points represent the taxi pick-up activity locations. But the mean centres of the drop-off locations and pick-up locations are quite near to each other; we may use the extended second moments of activity locations measurement category to analyze this relationship and distribution.

Drop-off locations, pick-up locations, SDE, and mean centre figure of one taxi vehicle operation. The convex hull figure of one taxi vehicle operation.

We will analyze these relationships: An illustration of the locations mean centre of each taxi driver and the all taxi drivers. The next part is how to calculate the Great-circle distance between two points on the globe surface.

Here we adopt the simple spherical law of cosines formula to calculate it [ 3132 ], assuming the Erath as a spherical earth ignoring the ellipsoidal effects. The spherical law of cosines formula gives well-conditioned results down to distances as small as around 1 meter.PREPRINT - ACCEPTED PAPER IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban.

Responses to EIA’s Electric Power Monthly – October Edition with data for August. Download-Theses Mercredi 10 juin The one process ongoing that will take millions of years to correct is the loss of genetic and species diversity by the destruction of natural habitats.

Smog is a type of air ph-vs.com word "smog was coined in the early 20th century as a portmanteau of the words smoke and fog to refer to smoky fog, its opacity, and odour.

The word was then intended to refer to what was sometimes known as pea soup fog, a familiar and serious problem in London from the 19th century to the midth century.

This kind of visible air pollution . Taxis share a high proportion of urban traffic volume and contribute a large proportion to urban air pollution. This paper addressees this context by exploring urban taxi air pollution emissions and possible reduction countermeasures.

Urban taxis and air pollution a case study in harbin china
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