Study on the environmental benefits of truck parking space for trucks waiting to enter freight terminal - A case study of Gangchen Logistics Park

Yunzhu Yan; Zhongning Fu; Jintian Yue

doi:10.61089/aot2024.jt891v87

Authors

Yunzhu Yan Lanzhou Jiaotong University, School of Traffic and Transportation, Lanzhou, 730070, China Author https://orcid.org/0009-0006-4479-5986
Zhongning Fu School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, China Author https://orcid.org/0009-0009-4823-3624
Jintian Yue School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, China Author https://orcid.org/0009-0004-4511-2687

DOI:

https://doi.org/10.61089/aot2024.jt891v87

Keywords:

freight terminal, truck parking space, exhaust emissions, VISSIM and MOVES

Abstract

The gathering effect of trucks at freight terminals and the high emission characteristics of trucks themselves lead to long-term truck queues and serious environmental pollution problems at freight terminals. Repeated stopping and following trucks aggravate the environmental pollution in the terminal. This paper proposes a scheme to plan truck parking spaces during waiting for trucks to enter the freight terminal in order to reduce the repeated stopping of trucks and to quantify the environmental benefits of truck parking spaces in the process. The truck admission process was simulated using the Visual Simulator (VISSIM). The simulation output provided vehicle operating data that was converted into operating mode distribution data using the Python calculation module. Then, the model parameters, including the operating mode distribution, were entered into the Motor Vehicle Emission Simulator (MOVES) to calculate the simulation scenario. Gangchen Logistics Park, for example, statistics of the park's 10 months of freight data, the design of freight volume gradually increased by 11 groups of admission simulation experiments, the simulation learned that the park queuing significant, more than 90% probability of queuing, queuing up to a maximum of 203 vehicles, the average queue of 61 vehicles. Then according to the actual road conditions in the park, add a parking lot in the VISSIM simulation. Signal sensing is realized by calling the COM interface of VISSIM through Python to guide the vehicles to park in order and enter the park. Eleven sets of simulation control experiments after designing and planning parking spaces are designed to calculate the pollutant emissions for each simulation scenario separately. The analysis of the emission measurement results shows that the emissions of CO, HC, NO_X, and PM10 can be reduced by 1.90%, 7.90%, 9.42%, and 10.55% at the average level of the park's cargo volume. At the park's maximum cargo volume, it is possible to reduce HC, NO_X, and PM10 emissions by about one-third. Truck parking space in the truck waiting to drive into the freight terminal process has obvious environmental benefits, queuing significant freight terminals should be reasonably planned truck parking spaces to reduce the freight terminal exhaust emissions pollution.

References

1. Bebkiewicz, K., Chłopek, Z., Sar, H.(2021). Assessment of impact of vehicle traffic conditions: urban, rural and highway, on the results of pollutant emissions inventory. Archives of Transport, 60(4), 57–69. https://doi.org/10.5604/01.3001.0015.5477.

2. Xu, T., Chen, S., Peng, C. (2023). Par-ameter relationship between exhaust em-issions and driving behaviors for com-mercial heavy-duty vehicles. Journal of Automotive Safety and Energy, 14(03), 365-374. https://doi.org/10.3969/j.issn.1674-8484.2023.03.012.

3. Xia, J., Li, J., Xie, J. (2023). Research Progress on Motor Vehicle Pollution Emission Models. Modern Industrial Economy and Informationization, 13(06), 307-311. https://doi.org/10.16525/j.cnki.14 -1362/n.2 023.06.102.

4. Li, T., Wang, X., Ling, J. (2021). Effects of Vehicle Load and Air Conditioner on Real Driving Emissions of Heavy-duty Vehicle. Small Internal Combustion Engine and Vehicle Technique, 50(01), 75-78. https:// doi.org/10.3969/j.issn.1671-0630. 2021.01. 016.

5. Madziel, M., Campisi, T. (2022). Assess-ment of vehicle emissions at roundabouts: A comparative study of PEMS data and microscale emission model. Archives of Transport, 63(3), 35-51. https://doi.org/10. 5604/0 1.3001.0015.992.

6. Yang, C., Xu, D., Zhang, K. (2021). Com-parative Study on Pollutant Emissions of Heavy-duty Vehicle Road Method and Chassis Dynamometer. Auto Sci-Tech, (04), 48-53. https://doi.org/10.3969/j.issn.1005-2550.2021.04.008.

7. Wang, Q., Cao, F., Fu, M.(2022). Emission factors of carbon dioxide from in-use vehicles based on bench testing. Journal of Nanjing University of Information Science & Technology (Natural Science Edition), 14(02), 156-166. https://doi.org/1013878/j. cnki.jnuist.2022.02.004.

8. Liu, Y. (2022). Research on Optimization of Bus Priority Using Pre-signals at Inters-ection Based on Exhaust Emissions. BEIJ- ING JIAOTONG UNIVERSITY. https://doi.org/10.26944/d.cnki.gbfju.2022.002855.

9. Zhang, L., Hu, X., Qiu, R. (2017). A Revi-ew of Research on Emission Models of V- ehicle Ex-hausts. World Sci-Tech R & D, 39 (04), 355-362. https://doi.org/10.16507/j. issn.1006-6055.2017.05.003.

10. LEJRI, D., CAN, A., SCHIPER, N. (2018). Accounting for traffic speed dynamics wh-en calculat-ing COPERT and PHEM pollut- ant emissions at the urban scale.Transport-ation Research Part D: Transport and Environment, 63, 588-603. https://doi.org/10.1016/j.trd.2018.06.023.

11. Andrych-Zalewska, M., Chłopek, Z., Merkisz J. (2022). Analysis of the operati- on states of inter-nal combustion engine in the Real Driving Emissions test. Archives of Transport, 61(1), 71-88. https://doi.org/10.5604/01.3001.0015.8162.

12. Li, Z. (2020). Vehicle Trajectory Reconst- ruction Model Based on Acceleration Dist- ributions for Emission Estimation, BEI- JING JIAOTONG UNIVERSITY. https://do i.org/10.26944/d.cnki.gbfju.2020.000220

13. Kim, M., Kim, H. (2020). Investigation of environmental benefits of traffic signal co- untdown timers. Transportation Research Part D,85. https://doi.org/10.1016/j.trd.202 0.102464.

14. Noriega, M., Trejos, E., Toro, C. (2023). Impact of oxygenated fuels on atmospheric emissions in major Colombian cities. Atmospheric Environment. https://doi.org/ 10.1016/j.atmosenv.2023.119863.

15. Abdelmegeed, M., Rakha, H. (2017). Hea-vy duty diesel truck emissions modeling. Transportation Research Record: Journal of the Transportation Research Board, 2627(1), 26-35. https://doi.org/10.3141/26 27-04.

16. Shan, X., Liu, H., Zhang, X. (2021). Local- ization of Light-Duty Vehicle Emission Factor Estima-tion Based on MOVES. Journal of Tongji University (Natural Science), 49(08), 1135-1143+1201. https://doi.org/10.11908/j.issn.0253-374x.21015.

17. Zhang, S., Yu, L., Song, G. (2017). Emiss-ions characteristics for heavy-duty diesel trucks under different loads based on vehicle-specific power. Transportation Res-earch Record: Journal of the Transportat-ion Research Board, 2627(1), 77-85. https://doi.org/10.3141/2627-09.

18. Huang, Y., Song, G., Peng, F. (2023). Sca-led Tractive Power Distribution and Emission Model for Heavy-duty Trucks Based on Vehicle Weight. Journal of Transportation Systems Engineering and Information Technology, 23(02), 326-334. https://doi.org/10.16097/j.cnki.1009-6744. 2023.02.034.

19. Song, G., Lv, H., Li, Z. (2021). Correction Errors Control of Emission Factors Speed Based on VSP Distributions. Journal of Transportation Systems Engineering and Information Technology, 21(06), 272-282. https://doi.org/10.16097/j.cnki.1009-6744. 2021.06.031.

20. Jia, X., Qin, X., Zhou, W. (2022). Analysis of Influence of Highway Slope Difference on Vehicle Exhaust Emission. Science Technology and Engineering, 22(03), 1265 -1270. https://doi.org/10.3969/j.issn.1671-1815.2022.03.053.

21. Yue, Y., Song, G., Huang, G. (2013).Appl-ication of MOVES in the Microscopic Evaluation of Traffic Emissions. Journal of Transport Information and Safety, 31 (06), 47-53. https://doi.org/10.3963/j.issn1674-4861.2013.06.010.

22. Hu, M., Shi, X., Zhai, S.(2021). Evaluation of Traffic and Environmental Benefits for the Mixed Traffic Flow of Autonomous Vehicles. Journal of Chongqing Jiaotong University(Natural Sci-ence), 40(08), 7-14. https://doi.org/10.3969/j.issn.1674-0696.20 21.08.02.

23. Li, J. (2022). Research on the Estimation Model of Total Motor Vehicle Exhaust Emissions at Sig-nal Intersections. High- ways & Automotive Applications, (03), 37-44. https://doi.org/10.20035/j.issn.1671-2668.2022.03.010.

24. Sun, L., Chen, Y., Kong, D. (2021). A Simulation Evaluation of Traffic Flow Efficiency of Urban Expressways under Cooperative Vehicle-infrastructure Scenarios. Journal of Transport Infor-mation and Safety, 39(01), 155-163. https://doi.org/10. 3963/j.issn.1674-4861.2021.01.0018.

25. Zhang, H., Sun, Y., Ma, B. (2024) Reliability-based design of length for auxiliary lane at dual-lane highway exits. Journal of Jilin University (Engineering and Technology Edition), 1-8 [2024-04-30]. https://doi.org/10.13229/j.cnki.jdxbgxb.20231226.

26. Shan, X., Chen, X. (2021). Review of Studies that Integrate Traffic-simulation Models with Micro-scopic Vehicle- emissions Models. Journal of Transportat-ion Engineering and Information, 19(02), 11-24. https://doi.org/10.3969/j.issn.1672-4747.2021.02.002.

27. Zhong, H., Yu, F., Liao, S. (2023). Develo- pment and evaluation of methane emission factor model for light-duty gasoline vehicles based on on-road driving tests. Acta Scientiae Circumstan-tiae, 43(06), 176 -184. https://doi.org/10.13671/j.hjkxxb.202 2.0383.

28. Wang, W., Bie, J., Yusuf, A. (2023). A new vehicle specific power method based on internally observable variables: Appli- cation to CO2 emission assessment for a hybrid electric vehicle. Ener-gy Conversion and Management, 286. https://doi.org/10. 1016/j.enconman.2023.117050.

29. Chen, J.(2020). Development of Simplified Vehicle Fuel Consumption Model Based on Vehicle Weight. BEI- JING JIAOTONG UNIVERSITY. https://doi.org/10.26944/d. cnki.gbfju.2019.000991.

30. Yu, J., Xiong, X., Liu, J. (2020). Compar-ison between Pems Test Cycle and China Heavy-Duty Commercial Vehicle Test Cycle. Vehicle Engine, (05), 80-86. https://doi.org/10.3969/j.issn.1001-2222.2020.05.013.

31. Ma, L., Zhou, J., Zhang, Y. (2023). Identi- fication Method of Employment Center Station in Urban Rail Transit Based on Passenger Flow Characteristics. Urban Mass Transit, 26(11), 59-64. https://doi. org/10.16037/j.1007-869x.2023.11.011.

32. Cao, Y., Guo, Y., Cao, G. (2017). A Study on Localization of Project-level Parameters of MOVES Model in Shenzhen. Journal of Transport Information and Safety, 35(02), 100-108. https://doi.org/10.3963/j.issn.167 4-4861.2017.02.015.