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国际学术期刊
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国际学术期刊
Trips and their CO2 emissions to and from a shopping center
发布时间:2013-12-615:0:23来源:作者:Tao Jia, Kenneth Carling, Johan Håkansson   

Tao Jiaa, c,
Kenneth Carlingb,
Johan Håkanssonb,
a School of Remote Sensing and Information Engineering, Wuhan University, China
b School of Technology and Business Studies, Dalarna University, Sweden
c Division of Geomatics, University of Gävle, Sweden


Highlights

•We extract trips to and from a shopping center and measure their CO2 emissions.
•We reveal hourly-based CO2 emissions during weekdays and weekends.
•We report a heterogeneous distribution of CO2 emissions in spatial areas and streets.
•We find that most of the trips follow an optimal route in terms of CO2 emissions.
•The shopping center is well located with low CO2 emissions via relocation planning.


Keywords
GPS tracking data; Trips; CO2 emissions; Relocation planning


Abstract
Previous studies have focused on entire trips within a geographical region, while only a few have examined trips to and from a city landmark. This paper examines trips and their CO2 emissions to and from a shopping center from a time–space perspective, and it further considers how this information can be used in relocation planning. It is a case study in the Borlänge city in mid-Sweden where trips to the city’s largest shopping mall are scrutinized. We use GPS tracking data of car trips starting and ending at the shopping center. Firstly, we analyze the traffic emission patterns from a time–space perspective where the temporal patterns reveal hourly-based traffic emission dynamics. The spatial analysis uncovers a heterogeneous distribution of traffic emissions in spatial areas and individual street segments. Secondly, we find the observed trips mostly agree with an optimal route in terms of CO2 emissions. Drawing on this finding, we thirdly evaluate the location of the current shopping center by comparing it to two competing locations. We conclude that the two competing locations, being in the vicinity of the current one, would induce an insignificant improvement in terms of CO2 emissions.



Article Outline
1. Introduction
2. Trips and the measurement of CO2 emissions
2.1. Trips
2.2. On measuring CO2 emissions

3. Time–space patterns of CO2 emissions
3.1. Temporal patterns
3.2. Spatial patterns

4. Optimal trips and evaluation of the location of the shopping center
4.1. Optimal trips with low energy cost
4.2. Evaluation of the location of the shopping center

5. Limitations
6. Conclusions
Acknowledgements
References



Figures
   

Fig. 1.

Illustration of identifying trips in a volunteer’s movement trajectory.


Fig. 2.

Density map of the 498 observed trips.


Fig. 3.

Hourly traffic CO2 emissions for weekdays and weekends.


Fig. 4.

Hourly trips for weekdays and weekends.


Fig. 5.

Maps of emissions during four peak periods on weekdays and weekends.


Fig. 6.

Map of observed CO2 emissions in different spatial areas visualized in three categories: low, medium, and high emissions.


Fig. 7.

Map of observed CO2 emissions in individual street segments visualized with three categories: low emission, medium emission, and high emission.


Fig. 8.

Log–log plot of the CO2 emissions (kg) in (a) trips (alpha = 2.62, xmin = 0.57, P = 0.11) and (b) street segments (alpha = 1.76, rate = 1.10, xmin = 0.053, P = 0.90).


Fig. 9.

Comparison of observed emission values with the optimal ones.


Fig. 10.

Maps of CO2 emissions (a) for numerous potential locations and (b) in single street segments induced by the new location obtained from 1-median model.



Tables

Table 1. Distribution of the number of trips per volunteer.

Table 2. Number of halts (tortuous locations) and street intersections per trip (note: for the first column in this table, we observed that 351 trips had zero halts with 11 as the average number street intersections’ crossings).

Table 3. Distribution of the estimated emission values per trip (g/km).

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