Schedule optimization to improve trunk-local bus transfer efficiency in small conurbations: A case study of New York’s capital region

Da-peng Zhang , Xiao-kun Wang

Journal of Central South University ›› 2016, Vol. 23 ›› Issue (7) : 1817 -1822.

PDF
Journal of Central South University ›› 2016, Vol. 23 ›› Issue (7) : 1817 -1822. DOI: 10.1007/s11771-016-3235-5
Geological, Civil, Energy and Traffic Engineering

Schedule optimization to improve trunk-local bus transfer efficiency in small conurbations: A case study of New York’s capital region

Author information +
History +
PDF

Abstract

Fostering the use of transit has been broadly accepted as an effective way to improve social equity and reduce the externalities caused by transportation. In the great body of transit literature, many have focused on the improvement of transfer efficiency. However, investigation on transit transfer efficiency is still lacking for medium sized cities or suburban areas that have sprawled from city centers. The special features associated with such an urban form lead to unique travel patterns and bus operations. This work develops a process to improve bus transfer efficiency for small conurbations considering their special characteristics. A case study of New York’s Capital District is used to illustrate the proposed method. Results show that the transfer waiting time can be remarkably shortened. The proposed method can be widely adapted to other transit systems in small conurbations.

Keywords

transfer efficiency / small conurbation / rural transit

Cite this article

Download citation ▾
Da-peng Zhang, Xiao-kun Wang. Schedule optimization to improve trunk-local bus transfer efficiency in small conurbations: A case study of New York’s capital region. Journal of Central South University, 2016, 23(7): 1817-1822 DOI:10.1007/s11771-016-3235-5

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

GuihaireV, HaoJ K. Transit network design and scheduling: A global review [J]. Transportation Research Part A: Policy and Practice, 2008, 42(10): 1251-1273

[2]

CepedaM, CominettiR, FlorianM. A frequency-based assignment model for congested transit networks with strict capacity constraints: characterization and computation of equilibria [J]. Transportation Research Part B: Methodological, 2006, 40(6): 437-459

[3]

HeJ, LiuZ. Transfer convenience of urban rail transit based on passengers’ physical and conscious resistance [C]. Service Operations and Logistics, and Informatics, IEEE/SOLI 2008. IEEE International Conference on. Beijing: IEEE, 20081536-1540

[4]

ZhangG, ChenY, LiP, FibbeS. Study on evaluation indicators system of crowd management for transfer stations based on pedestrian simulation [J]. International Journal of Computational Intelligence Systems, 2011, 4(6): 1375-1382

[5]

GuoZ, WilsonN H. Assessing the cost of transfer inconvenience in public transport systems: A case study of the London underground [J]. Transportation Research Part A: Policy and Practice, 2011, 45(2): 91-104

[6]

GoverdeR MImproving punctuality and transfer reliability by railway timetable optimization [D], 1999DelfDepartment of Technical Mathematics, Faculty of Information Technology and Systems, Delft University of Technology

[7]

HadasY, CederA. Public transit network connectivity [J]. Transportation Research Record: Journal of the Transportation Research Board, 2010, 2143(1): 1-8

[8]

WardmanM. Public transport values of time [J]. Transport Policy, 2004, 11(4): 363-377

[9]

CevallosF, ZhaoF. Minimizing transfer times in public transit network with genetic algorithm [J]. Transportation Research Record: Journal of the Transportation Research Board, 2006, 1971(1): 74-79

[10]

HsuS C. Determinants of passenger transfer waiting time at multi-modal connecting stations [J]. Transportation Research Part E: Logistics and Transportation Review, 2010, 46(3): 404-413

[11]

ShrivastavP, DhingraS. Development of feeder routes for suburban railway stations using heuristic approach [J]. Journal of Transportation Engineering, 2001, 127(4): 334-341

[12]

GiulianoG, NarayanD. Another look at travel patterns and urban form: the US and Great Britain [J]. Urban Studies, 2003, 40(11): 2295-2312

[13]

KrizekK J. Residential relocation and changes in urban travel: Does neighborhood-scale urban form matter? [J]. Journal of the American Planning Association, 2003, 69(3): 265-281

[14]

SteadD, MarshallS. The relationships between urban form and travel patterns. An international review and evaluation [J]. European Journal of Transport and Infrastructure Research, 2001, 1(2): 113-141

[15]

Merriam-Webster. Conurbation. [EB/OL].[2014-07-31]. http://www. merriam-webster.com/dictionary/conurbation.

[16]

CederA, GolanyB, TalO. Creating bus timetables with maximal synchronization [J]. Transportation Research Part A: Policy and Practice, 2001, 35(10): 913-928

[17]

FredaP, SchmierK JPublic transit vehicle arrival information system, 1999

[18]

DaileyD, MacieanS, CatheyF, WallI. Transit vehicle arrival prediction: Algorithm and large-scale implementation [J]. Transportation Research Record: Journal of the Transportation Research Board, 2001, 1771(1): 46-51

[19]

LawP, TaylorB D. Shelter from the storm: Optimizing distribution of bus stop shelters in Los Angeles [J]. Transportation Research Record: Journal of the Transportation Research Board, 2001, 1753(1): 79-85

[20]

TyreeJ DAssessing transit service performance: Recommended performance standards for the Santa Clara Valley transportation authority [R], 2010San JoseAmerican Public Transportation Association

[21]

IsekiH, TaylorB D. Style versus service? An analysis of user perceptions of transit stops and stations [J]. Journal of Public Transportation, 2010, 13(3): 23-48

[22]

LitmanT. Valuing transit service quality improvements [J]. Journal of Public Transportation, 2008, 11(2): 43-63

[23]

TeodorovicD, LucicP. Schedule synchronization in public transit using the fuzzy ant system [J]. Transportation Planning and Technology, 2005, 28(1): 47-76

[24]

CevallosF, ZhaoF. A genetic algorithm for bus schedule synchronization. in applications of advanced technology in transportation [C]. The Ninth International Conference of ASCE, Chicago: ASCE, 2006737C742

[25]

TingC J, SchonfeldP. Schedule coordination in a multiple hub transit network [J]. Journal of Urban Planning and Development, 2005, 131(2): 112-124

[26]

ShrivastavaP, DhingraS. Development of coordinated schedules using genetic algorithms [J]. Journal of Transportation Engineering, 2002, 128(1): 89-96

[27]

SunL, RongJ, YaoL. Measuring transfer efficiency of urban public transportation terminals by data envelopment analysis [J]. Journal of Urban Planning and Development, 2010, 136(4): 314-319

[28]

City-Data. Troy, NY. [EB/OL]. [2014-07-31]. http://www.city-data. com/city/Troy-New-York.html.

[29]

CDTA. About CDTA. [EB/OL].[2014-07-31]. https://www.dta.org/ bout. hp.

[30]

Capital District Transportation Authority. Routes and schedule. [EB/OL]. [2012-03-01]. http://www.cdta.org/.

[31]

European Union Regulation. Regulation (EC) No 561/2006 of the European parliament and of the Council of 15 March 2006 on the harmonisation of certain social legislation relating to road transport and amending Council Regulation (EEC) No 3821/85 and (EC) No 2135/98 and repealing Council Regulation (EEC) No 3820/85. [EB/OL]. [2014-07-24]. http://eur-lex.europa.eu/legal-ontent/EN/N OT/?uri=CELEX:32006R0561.

AI Summary AI Mindmap
PDF

95

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/