Empirical Analysis of Relieving High-Speed Rail Freight Congestion in China
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Model Assumptions
- (1)
- This paper considers B, T, J, X, N, Su, and S as the representative of main cities according to their economic scale. Since the volume of express delivery departs from main cities in the B-S HSR corridor was always the highest in November in recent years (Figure 1), this paper assumes November is always the peak month of HSR freight.
- (2)
- To best centralize capacity and simplify the service procedure during the peak month from 2019 to 2022, Company C usually only keeps one type of service with a fixed discounted price. Therefore, this paper assumes the service quality and price of HSR freight are fixed in the peak month.
- (3)
- This paper assumes that the economy of each city develops independently.
- (4)
- This paper assumes the inspecting train that runs between city B and S has a fixed operating route, capacity, and transport volume during the peak month from 2019 to 2022.
- (5)
- This paper assumes the available number of HSR passenger trains for freight is unchanged during the peak month from 2019 to 2022, and no more than one carriage per train can be used for freight.
- (6)
- This paper assumes it is feasible for all the HSR passenger trains to operate under mixed mode and reserved mode. Since HSR trains cannot stop for enough time at small cities to assemble a large number of parcels, this paper assumes Company C only adopts the reserved mode between main cities.
3.2. Data Collection
3.3. Forecast Methodology
3.3.1. Build Predictive Regression Model
3.3.2. Forecast Time Series Predictor of Regression Model
4. Result and Discussion
4.1. Analysis of Influential Factor on the Flow
4.2. Analysis of Parcel Flow Volume Forecast
4.3. Analysis of Mode Adoption
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. Process of Dimension Reduction
VAR | |||||||||
VIF | 233.6 | 306.6 | 55.2 | 277.9 | 31.1 | 14.4 | 12.5 | 32.0 | 1.2 |
VAR | |||||||||
VIF | 210.6 | 488.1 | 59.3 | 306.4 | 13.9 | 9.7 | 10.8 | 57.5 |
Appendix B. Process of Selecting the Best Predictive Regression Model
Model1 | Model2 | Model3 | Model4 | Model5 | Model6 | |
---|---|---|---|---|---|---|
Intercept | 1.78 ** | −14.72 *** | −28.68 *** | −21.58 *** | −22.54 *** | −21.72 *** |
0.83 *** | 1.25 *** | 1.24 *** | 1.23 *** | 1.2 *** | ||
0.89 *** | 0.93 *** | 0.93 *** | 0.90 *** | 0.89 *** | ||
0.82 *** | ||||||
0.71 * | 2.23 ** | 2.22 *** | 2.14 *** | |||
−1.70 *** | −1.71 ** | −1.79 ** | ||||
0.27 | 0.24 | |||||
0.24 | ||||||
0.13 | 0.47 | 0.51 | 0.52 | 0.52 | 0.52 | |
BP Test (p-value) | 0.00 | 0.20 | 0.69 | 0.44 | 0.84 | 0.59 |
BG Test (p-value) | 0.00 | 0.61 | 0.31 | 0.31 | 0.29 | 0.20 |
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Category | Variable | Unit | Description | Data Source |
---|---|---|---|---|
Response variables | VPH | ton | Volume of parcels transported by high-speed rail (HSR) in 2018/11 | A railway carrier of China (not publicly available) |
Distance Variable | DIS | km | HSR Distance between cities | http://search.huochepiao.com/ |
Variables represents cities’ economic and postal scale | GDP | billion RMB | Gross domestic product (1998–2018) | Each city’s Statistical Communique on National Economic and Social Development |
POP | million persons | Permanent resident population (1998–2018) | ||
AVI | billion RMB | Added value of the tertiary industry (1998–2018) | ||
URG | billion RMB | Urban retail sales of consumer goods (1998–2018) | ||
VPC | ton | Volume of parcels sent from city (2014/07–2020/02) | The official website of each city’s Postal Administration | |
Variables represents the HSR facility capacity of city | LEV | point | The total level score of all HSR stations of city | Regulations of the People’s Republic of China on Railway Technical Management |
MHD | train | The mean of HSR trains departing from city between peak and normal period of passenger traffic | A railway company in China and the official website of China railway passenger service center: https://www.12306.cn/index/ | |
MHA | train | The mean of HSR trains arriving at city between peak and normal period of passenger traffic | ||
VPD | person | The max variation of passengers departing from the city between peak and normal period of passenger traffic | ||
VPA | person | The max variation of passengers arriving at the city between peak and normal periods of passenger traffic |
Method | Description |
---|---|
Exponential smoothing (ETS) | The basic form is weighted averages of past observations, with the weights decaying exponentially as the observations get older [25]. |
Auto Regression Integrated Moving Average model (ARIMA) | The basic form is weighted averages of past values of the forecast variable and past forecast errors. It is also capable of modeling seasonal data by including additional seasonal terms (SARIMA). |
Seasonal and Trend Decomposition using Loess (STL) | The basic form is used for decomposing time series data, which shows some seasonality [26]. |
ETS | ARIMA | STL + ETS | STL + ARIMA | COMB | Best | |
---|---|---|---|---|---|---|
B | 176 | 209 | 212 | 209 | 198 | ETS |
T | 77 | 84 | 81 | 67 | 72 | STL + ARIMA |
J | 95 | 83 | 59 | 60 | 70 | STL + ETS |
X | 39 | 57 | 38 | 69 | 47 | STL + ETS |
N | 102 | 110 | 83 | 83 | 85 | STL + ETS |
Su | 239 | 271 | 230 | 225 | 200 | COMB |
S | 212 | 356 | 216 | 447 | 271 | ETS |
Rank | Origin | Destination | Distance (km) | Flow Volume (ton) Nov. 2018 | Flow Volume (ton) Nov. 2019–Nov. 2022 |
---|---|---|---|---|---|
1 | B | S | 1318 | 48.13 | 122.27~191.65 |
2 | B | J | 426 | 42.11 | 34.70~44.69 |
3 | S | B | 1318 | 34.43 | 110.13~323.28 |
4 | J | B | 426 | 14.17 | 16.23~20.24 |
5 | T | S | 1223 | 13.19 | 28.50~44.68 |
6 | S | T | 1223 | 12.45 | 26.11~98.02 |
7 | N | B | 1023 | 9.37 | 35.02~45.96 |
8 | B | T | 95 | 3.95 | 23.12~28.83 |
9 | B | X | 692 | 3.91 | 15.02~25.30 |
10 | X | B | 692 | 3.33 | 31.70~35.23 |
11 | N | S | 295 | 2.71 | 27.67~39.97 |
12 | S | J | 892 | 2.27 | 21.32~39.97 |
13 | B | N | 1023 | 1.37 | 27.48~37.43 |
14 | J | S | 892 | 1.34 | 24.39~37.08 |
15 | C | S | 165 | 1.14 | 5.61~8.48 |
Direction | Segment | Volume Forecast 2019–2022 (ton/month) | Available Number (trains/day) | Allocation | |
---|---|---|---|---|---|
Mixed Mode (trains/day) | Reserved Mode (carriages/day) | ||||
Down | B–T | 242~353 | 115 | 106.7~110.7 | 4.3~8.3 |
T–J | 259~390 | 109 | 99.2~103.9 | 5.1~9.8 | |
J–X | 251~384 | 96 | 85.9~90.7 | 5.3~10.1 | |
X–N | 244~369 | 101 | 91.6~96.1 | 4.9~9.4 | |
N–Su | 233~353 | 122 | 114.0~118.4 | 3.6~8.0 | |
Su–S | 223~342 | 160 | 153.9~158.2 | 1.8~6.1 | |
Average | 242~365 | 117 | 108.6~113.0 | 4.2~8.6 | |
Up | S–Su | 189~533 | 118 | 104.8~116.5 | 1.5~13.2 |
Su–N | 179~520 | 122 | 109.4~121.1 | 0.9~12.6 | |
N–X | 226~559 | 101 | 86.2~97.6 | 3.4~14.8 | |
X–J | 262~600 | 101 | 84.7~96.2 | 4.8~16.3 | |
J–T | 250~559 | 128 | 112.8~124.0 | 4.0~15.2 | |
T–B | 203~437 | 149 | 139.0~147.5 | 1.5~10.0 | |
Average | 218~534 | 120 | 106.2~117.1 | 2.7~13.6 |
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Gao, H.; Zhang, M.; Goodchild, A. Empirical Analysis of Relieving High-Speed Rail Freight Congestion in China. Sustainability 2020, 12, 9918. https://doi.org/10.3390/su12239918
Gao H, Zhang M, Goodchild A. Empirical Analysis of Relieving High-Speed Rail Freight Congestion in China. Sustainability. 2020; 12(23):9918. https://doi.org/10.3390/su12239918
Chicago/Turabian StyleGao, Hanlin, Meiqing Zhang, and Anne Goodchild. 2020. "Empirical Analysis of Relieving High-Speed Rail Freight Congestion in China" Sustainability 12, no. 23: 9918. https://doi.org/10.3390/su12239918
APA StyleGao, H., Zhang, M., & Goodchild, A. (2020). Empirical Analysis of Relieving High-Speed Rail Freight Congestion in China. Sustainability, 12(23), 9918. https://doi.org/10.3390/su12239918