Using Large-Scale Truck Trajectory Data to Explore the Location of Sustainable Urban Logistics Centres—The Case of Wuhan
Abstract
:1. Introduction
2. Literature Review
2.1. Logistic Centre Location Problem
2.2. Application of Truck Trajectory Data
3. Research Methodology
3.1. Overview of the Methodology
3.2. Extraction of Truck Original and Destination Points Based on Truck Trajectory Data
3.3. Using the DBSCAN Clustering Algorithm to Obtain Freight Demand in Cities
3.4. Optimal Site Selection Based on an Improved P-Median Site Selection Model
- (1)
- Objective function
- (2)
- Constraint functions
4. Study Area and Data Pre-Processing
4.1. Study Area
4.2. Data Pre-Processing
4.3. Model Constraints
- (1)
- The sets of supply points (N) and demand points (M). Supply point N indicates the set of planned logistics storage land and current logistics centres in Wuhan, and there are 23 of them. As of 2021, this paper takes the logistics centres in the previous Wuhan logistics spatial layout plan as the current logistics centres, and there are 14 logistics centres. Meanwhile, the planned and unbuilt logistics and warehousing sites in Wuhan City are selected as alternative locations, and there are 9 locations in total. Taking the current logistics centres and alternative logistics and warehousing sites in Wuhan as alternative locations for the experiment, 23 candidate locations are finally identified, and their spatial distribution and facility scale are shown in Figure 6, Table 3 and Table 4. Through our analysis, we obtained a total of 2410 clusters based on the DBSCAN cluster analysis and pre-processing of freight starting and destination points. In this paper, the geometric centroids of the clusters are extracted. They are considered as the areas in the city with high logistics demand and then used as demand points in the freight model. As a result, the set M includes 2410 demand points, whose spatial distribution is shown in Figure 7.
- (2)
- Number of facilities P with alternative points. To facilitate the analysis of the results before and after the layout optimization, the number of facilities P determined in this paper is still 14. The alternative points are selected as alternative locations for the planned and unbuilt logistics and warehousing sites in Wuhan, and there are 9 locations in total.
- (3)
- Road network dataset and travel costs . Through the analysis of trajectory data and in combination with the related literature, the trucks usually have a road transport-based travel mode, and the travel cost is measured by travel distance in km. This paper combines the vector data of highways, main roads, and secondary roads in Wuhan to construct a network dataset and establish topological relationships. As shown in Figure 8, the paper is based on the actual road network distances for the location of the logistics centre. In the existing layout plan, the average travel distance from the demand point to each logistics centre facility point in the city is 11.11 km, the maximum travel distance is 52.54 km, and the minimum travel distance is 0.03 km. Considering the average travel distance and the actual situation of the city, the maximum travel distance used in the model is 35 km.
- (4)
- The service capacity of the facility Q. In practice, the service capacity of logistics facilities is limited by their sizes. Through the research of the current logistics centre in Wuhan, the maximum service capacity is selected to be no more than 15% of the total, and the minimum service capacity is no less than 1% of the total. In summary, the maximum capacity parameter is set to 360, and the minimum capacity parameter is set to 24 in this paper.
- (5)
- Impedance coefficient . To determine the value of , this paper makes an accurate fit for the behavioural way of the demand point, and the results show that when , the behaviour of truck travel can be better represented, and the model results are more in line with the actual situation.
5. Experiment and Results
5.1. Site Optimization Results
5.2. Analysis of the Characteristics before and after Optimization
- (1)
- Newly sited logistics centres are mostly located in areas with good accessibility and high freight demand.
- (2)
- The travel cost of trucks has a greater impact on the siting of the logistics facility location, and the travel cost after optimisation is smaller than that before optimisation.
- (3)
- There is a significant reduction in the carrying capacity of the current logistics centre after optimisation, and the spatial allocation becomes more equitable.
6. Discussion of the Optimisation Results
6.1. Discussion from the Perspective of the Logistics Centres’ Administrator
6.2. Discussion from the Perspective of Policymakers
6.3. Discussion of the Results Achieved from the Current Study
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Field Name | Description |
---|---|
ID | Goods vehicle return number (unique identification) |
LNG | WGS 84 coordinate longitude |
LAT | WGS 84 coordinate longitude |
GPS_TIME | The time recorded by GPS equipment |
Speed | Instantaneous speed of the truck, in km/h |
Angle due north | Instantaneous angle calculated clockwise from due north |
Vehicle location | City/county/prefectural section name |
Miles travelled | Accumulated mileage, in km |
ID | LNG | LAT | GPS_TIME | Speed | Province_ID | CITY_ID | Angle | Miles Travelled |
---|---|---|---|---|---|---|---|---|
1005562496456470000 | 113.7077 | 29.61245 | 2018/6/1 5:25 | 79 | 421200 | 786627 | 40 | 1,991,699 |
1005562496456470000 | 113.7121 | 29.61653 | 2018/6/1 5:25 | 70 | 421200 | 786627 | 40 | 1,991,706 |
1005562496456470000 | 113.7162 | 29.62057 | 2018/6/1 5:27 | 71 | 421200 | 786627 | 40 | 1,991,712 |
1005562496456470000 | 113.7206 | 29.62479 | 2018/6/1 5:27 | 80 | 421200 | 786627 | 40 | 1,991,719 |
1005562496456470000 | 113.7255 | 29.62936 | 2018/6/1 5:27 | 75 | 421200 | 786627 | 30 | 1,991,726 |
1005562496456470000 | 113.729 | 29.63377 | 2018/6/1 5:27 | 75 | 421200 | 786627 | 30 | 1,991,732 |
1005562496456470000 | 113.7326 | 29.63859 | 2018/6/1 5:28 | 76 | 421200 | 786627 | 30 | 1,991,739 |
1005562496456470000 | 113.7373 | 29.64281 | 2018/6/1 5:28 | 77 | 421200 | 786627 | 50 | 1,991,746 |
1005562496456470000 | 113.7427 | 29.64598 | 2018/6/1 5:29 | 76 | 421200 | 786627 | 60 | 1,991,752 |
1005562496456470000 | 113.7487 | 29.64838 | 2018/6/1 5:29 | 75 | 421200 | 786627 | 60 | 1,991,759 |
1005562496456470000 | 113.7546 | 29.65075 | 2018/6/1 5:30 | 76 | 421200 | 786627 | 60 | 1,991,766 |
1005562496456470000 | 113.7605 | 29.65327 | 2018/6/1 5:30 | 77 | 421200 | 786627 | 60 | 1,991,772 |
1005562496456470000 | 113.7663 | 29.65649 | 2018/6/1 5:31 | 84 | 421200 | 786627 | 50 | 1,991,779 |
Serial Number | Logistics Centre |
---|---|
1 | Jiangnan Airport International Logistics Port |
2 | Hankou North Integrated Logistics Park |
3 | Tianhe Airport Integrated Logistics Park |
4 | Yangluo Port Integrated Logistics Park |
5 | Dongxihu Comprehensive Logistics Park |
6 | Zhengdian Integrated Logistics Park |
7 | Huashan Port Integrated Logistics Park |
8 | Gulong Logistics Centre |
9 | East Lake High-tech Development Zone Logistics Park |
10 | Beihu Logistics Centre |
11 | Jinkou Logistics Centre |
12 | Shamao Logistics Centre |
13 | Zhujiawan Logistics Centre |
14 | Changfu Logistics Centre |
Serial Number | Logistics Centre |
---|---|
15 | Dahualing Logistics Park |
16 | Tuoluokou Logistics Centre |
17 | Dengnan Port Logistics Park |
18 | Tonghang Logistics Park |
19 | Huangpi Logistics Centre |
20 | Zhucheng Logistics Centre |
21 | Jinkai Port Logistics Park |
22 | Dahuashan Logistics Park |
23 | Qingshan Logistics Centre |
Abbreviation | Site Location and Allocation Results Definition |
---|---|
Model 1 | Results for current logistics centres by the P-median method |
Model 2 | Results by the P-median method. |
Model 3 | Results by the improved P-median method. |
Model 4 | Results by the P-center model. |
Main Indicators | Model 1 | Model 2 | Model 3 | Model 4 |
---|---|---|---|---|
Average (km) | 11.12 | 8.61 | 8.72 | 8.67 |
Median (km) | 11.35 | 8.50 | 8.58 | 8.59 |
Maximum value (km) | 52.56 | 47.41 | 34.44 | 34.44 |
<5 km (%) | 18.3 | 29.1 | 29.2 | 28.7 |
<10 km (%) | 41.8 | 59.1 | 58.1 | 58.5 |
<20 km (%) | 94.9 | 98.8 | 98.5 | 99 |
<30 km (%) | 98.9 | 99.6 | 99.9 | 99.8 |
Proportion for services (%) | 99.9 | 99.9 | 99.4 | 99.8 |
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Jiao, H.; Yang, F.; Xu, S.; Huang, S. Using Large-Scale Truck Trajectory Data to Explore the Location of Sustainable Urban Logistics Centres—The Case of Wuhan. ISPRS Int. J. Geo-Inf. 2023, 12, 88. https://doi.org/10.3390/ijgi12030088
Jiao H, Yang F, Xu S, Huang S. Using Large-Scale Truck Trajectory Data to Explore the Location of Sustainable Urban Logistics Centres—The Case of Wuhan. ISPRS International Journal of Geo-Information. 2023; 12(3):88. https://doi.org/10.3390/ijgi12030088
Chicago/Turabian StyleJiao, Hongzan, Faxing Yang, Shasha Xu, and Shibiao Huang. 2023. "Using Large-Scale Truck Trajectory Data to Explore the Location of Sustainable Urban Logistics Centres—The Case of Wuhan" ISPRS International Journal of Geo-Information 12, no. 3: 88. https://doi.org/10.3390/ijgi12030088
APA StyleJiao, H., Yang, F., Xu, S., & Huang, S. (2023). Using Large-Scale Truck Trajectory Data to Explore the Location of Sustainable Urban Logistics Centres—The Case of Wuhan. ISPRS International Journal of Geo-Information, 12(3), 88. https://doi.org/10.3390/ijgi12030088