Estimation method of crowding cost in urban rail transit carriages

FANG De-wei; HE Dong-po; WANG Li-feng; CHEN Xi; SUN Xiang-long

doi:10.19818/j.cnki.1671-1637.2018.06.013

Volume 18 Issue 6

Turn off MathJax

Article Contents

Article Navigation > Journal of Traffic and Transportation Engineering > 2018 > 18(6): 121-130

Next Previous

FANG De-wei, HE Dong-po, WANG Li-feng, CHEN Xi, SUN Xiang-long. Estimation method of crowding cost in urban rail transit carriages[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 121-130. doi: 10.19818/j.cnki.1671-1637.2018.06.013

Citation:

FANG De-wei, HE Dong-po, WANG Li-feng, CHEN Xi, SUN Xiang-long. Estimation method of crowding cost in urban rail transit carriages[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 121-130. doi: 10.19818/j.cnki.1671-1637.2018.06.013

Citation:

PDF( 1348 KB)

Estimation method of crowding cost in urban rail transit carriages

doi: 10.19818/j.cnki.1671-1637.2018.06.013

School of Civil Engineering, Northeast Forestry University, Harbin 150040, Heilongjiang, China

More Information

Author Bio:
FANG De-wei(1978-), male, lecturer, PhD, fdw@nefu.edu.cn

HE Dong-po(1962-), male, professor, hdp@nefu.edu.cn
Received Date: 2018-07-28
Publish Date: 2018-12-25

Abstract

Abstract

The discrete choice analysis and contingent valuation method were used to design a double-bounded dichotomous questionnaire to calculate the total generalized cost of rail transit, including the crowding degrees.The passenger densities in the carriage was reduced by extending the travel time of passengers in the carriage, and the equivalent travel efficiencies were obtained under different crowding degrees in the carriage.The passengers' choice probability for two rounds of bidding was obtained from the questionnaire feedback.The bivariate probit estimators and random utility estimators were used to deduce the standardized value of the time marginal disutility, and then the time multiplier was obtained to estimate the passengers' willingness to travel longer and the willingness to pay.Based on the data obtained from the survey of 15 representative platforms of Beijing Metro Lines 1and 5in 2015-2016, a linear regression analysis was made for the time multiplier under six crowding degrees in the rail transit carriage.Analysis result shows that there is a linear relationship between the passenger density in the carriage and the time multiplier, but an inverse proportion between the crowding degrees after improvementand the generalized cost in the carriage.Traveler's willingness to pay increases with the reduction of crowding degree in the carriage.When the crowding degree improves from 5 person·m^-2 to 4 person·m^-2, the willingness to pay for peak hours in the morning and evening is RMB 1.58 and 3.02, respectively.When the crowding degree improves from 5 person·m^-2 to 3.5 person·m^-2, the willingness to pay for peak hours in the morning and evening increase to RMB 2.47 and 4.99, respectively.Therefore, traveler's average payment willingness in the evening peak is approximate twice that in the morning peak, and there are significant differences in the payment willingness for the improved crowding degree at different periods.
- urban rail transit,
- contingent valuation method,
- crowding degree,
- crowding cost,
- time multiplier,
- travel time,
- payment willingness

FullText(HTML)

References(27)

References

[1]	OLIO D L, IBEAS A, CECI'N P. Modelling user perception of bus transit quality[J]. Transport Policy, 2010, 17 (6): 388-397. doi: 10.1016/j.tranpol.2010.04.006
[2]	“Empirical Study of Public Transport Subsidies on Relieving Traffic Stress”Project Group. Analysis of urban public transport subsidies policy in China: a case study of Beijing[J]. China Collective Economy, 2013 (6): 42-45. (in Chinese). doi: 10.3969/j.issn.1008-1283.2013.06.020
[3]	NESHELI M M, CEDER A. Improved reliability of public transportation using real-time transfer synchronization[J]. Transportation Research Part C: Emerging Technologies, 2015, 60: 525-539. doi: 10.1016/j.trc.2015.10.006
[4]	SHI Rui-jia, MAO Bao-hua, DING Yong, et al. Pedestrian choice behavior analysis of standing position in subway carriage[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17 (2): 142-147, 159. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201702021.htm
[5]	TIRACHINI A, HENSHER D A, ROSE J M. Crowding in public transport systems: effects on users, operation and implications for the estimation of demand[J]. Transportation Research Part A: Policy and Practice, 2013, 53: 36-52. doi: 10.1016/j.tra.2013.06.005
[6]	XI Ya-jun, LIN Jian-hui, SU Yan-chen, et al. Subway car fire spreading rules and evacuation safety studies[J]. Journal of Railway Science and Engineering, 2017, 14 (3): 619-625. (in Chinese). doi: 10.3969/j.issn.1672-7029.2017.03.024
[7]	WANG Rui-shan, JIN Lan-tao. Generating characteristics and risk management of trampling accidents in metro places: an investigation of 15typical accidents since 2008[J]. Journal of People's Public Security University of China: Social Science Edition, 2016 (4): 141-148. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GADX201604020.htm
[8]	PEL A J, BEL N H, PIETERS M. Including passengers'response to crowding in the Dutch national train passenger assignment model[J]. Transportation Research Part A: Policy and Practice, 2014, 66: 111-126. doi: 10.1016/j.tra.2014.05.007
[9]	TIRACHINI A, HENSHER D A. Bus congestion, optimal infrastructure investment and the choice of a fare collection system in dedicated bus corridors[J]. Transportation Research Part B: Methodological, 2011, 45 (5): 828-844. doi: 10.1016/j.trb.2011.02.006
[10]	(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201212016.htm
[11]	WU Qi-bing, CHEN Feng, LIU Ou-yang, et al. Modeling and simulation of standing passengers'space comfort in subway carriage[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14 (5): 126-132. (in Chinese). doi: 10.3969/j.issn.1009-6744.2014.05.019
[12]	WU Qi-bing, CHEN Feng, Gao Yong-xin, et al. Computation model of standing-passenger density in urban rail transit carriage[J]. Journal of Traffic and Transportation Engineering, 2015, 15 (4): 101-109. (in Chinese). doi: 10.19818/j.cnki.1671-1637.2015.04.013
[13]	CANTWELL M, CAULFIELD B, O'MAHONY M, et al. Examining the factorsthatimpactpublictransport commuting satisfaction[J]. Journal of Public Transportation, 2009, 12 (2): 1-21. doi: 10.5038/2375-0901.12.2.1
[14]	TIRACHINI A, HENSHER A D, ROSE M J. Multimodal pricing and optimal design of urban public transport: the interplay between traffic congestion and bus crowding[J]. Transportation Research Part B: Methodological, 2014, 61: 33-54.
[15]	EBOLI L, MAZZULLA G. Relationships between rail passengers'satisfaction and service quality: a framework for identifying key service factors[J]. Public Transport, 2015, 7 (2): 185-201.
[16]	BATARCE M, MUOZ C J, ORT'UZAR D J. Valuing crowding in public transport: implications for cost-benefit analysis[J]. Transportation Research Part A: Policy and Practice, 2016, 91: 358-378. doi: 10.1016/j.tra.2016.06.025
[17]	KROES E, KOUWENHOVEN M, DEBRINCAT L, et al. On the value of crowding in public transport forle-de-France[R]. Paris: Organization for Economic Co-operation and Development, 2013.
[18]	HAYWOOD L, KONING M. The distribution of crowding costs in public transport: new evidence from Paris[J]. Transportation Research Part A: Policy and Practice, 2015, 77: 182-201.
[19]	LI Hao, GAO Kun, TU Hui-zhao. Variations in mode-specific valuations of travel time reliability and in-vehicle crowding: Implications for demand estimation[J]. Transportation Research Part A: Policy and Practice, 2017, 103: 250-263.
[20]	北京交通发展研究院. 2015北京市交通发展年度报告[R]. 北京: 北京交通发展研究院, 2015. Beijing Transport Institute. 2015Beijing Transport Annual Report[R]. Beijing: Beijing Transport Institute, 2015. (in Chinese).
[21]	北京交通发展研究院. 2016北京市交通发展年度报告[R]. 北京: 北京交通发展研究院, 2016. Beijing Transport Institute. 2016Beijing Transport Annual Report[R]. Beijing: Beijing Transport Institute, 2016. (in Chinese).
[22]	CAI Chun-guang, CHEN Gong, QIAO Xiao-chun, et al. Comparison of single bound and double bound dichotomous contingent valuation technique—a case of estimate health economic loss by air pollution of Beijing[J]. China Environmental Science, 2007, 27 (1): 39-43. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHJ200701010.htm
[23]	LU Hui, FOWKES T, WARDMAN M. Amending the incentive for strategic bias in stated preference studies case study in users'valuation of rolling stock[J]. Transportation Research Record, 2008 (2049): 128-135.
[24]	HENSHER D A. Hypothetical bias, choice experiments and willingness to pay[J]. Transportation Research Part B: Methodological, 2010, 44 (6): 735-752.
[25]	HAAB C T. Estimation using contingent valuation data from a “dichotomous choice with follow-up”questionnaire: a comment[J]. Journal of Environmental Economics and Management, 1998, 35: 190-194.
[26]	ALBERINI A, KANNINEN B, CARSON R T. Modeling response incentive effects in dichotomous choice contingent valuation data[J]. Land Economics, 1997, 73 (3): 309-324.
[27]	WANG Jing, LIU Jian-feng, MA Yi-lin, et al. Temporal and spatial passenger flow distribution characteristics at rail transit stations in Beijing[J]. Urban Transport of China, 2013, 11 (6): 18-27. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CSJT201306005.htm

Relative Articles

Supplements(0)

Cited By

Get Citation

PDF

XML

Article Metrics

Article views (926) PDF downloads(621)

Estimation method of crowding cost in urban rail transit carriages

doi: 10.19818/j.cnki.1671-1637.2018.06.013

Abstract

References

Catalog

Article Metrics

Related

Estimation method of crowding cost in urban rail transit carriages

doi: 10.19818/j.cnki.1671-1637.2018.06.013

Abstract

References

Catalog

Article Metrics

Related

Export File

Citation

Format

Content