How Many and Where to Locate Parking Lots? A Space–time Accessibility-Maximization Modeling Framework for Special Event Traffic Management

Abstract

Special event traffic planning and management needs to accommodate high traffic demand volume and special distribution patterns with dramatic structural deviations from the normal conditions. To provide sufficient transportation service supply that matches non-typical demand needs, this paper explains how to systematically optimize the locations of park-and-ride stations, the number of additional parking lots, and the bus rapid transit schedules. The goal is to maximize the number of travelers who can complete their activity tours within a reasonable travel time budget. Based on a space–time network construct, this paper formulates a network design problem to maximize the system-wide transportation accessibility from different origins to activity locations at special event sites. A linear integer programing model is proposed to formulate the joint optimization of the location and capacity of parking lots associated with mega-event sites. Illustrative and real-world examples are used to examine the effectiveness and practical usefulness of the proposed modeling framework.

Keywords

Special event management / Space–time accessibility / Park and ride / Dynamic network design / Dynamic traffic assignment

References

[1] Bovy P (2003) Mega sports event transportation and main mobility management issues. In: Transport and exceptional public events. Report of the one hundred and twenty second round table on transport economics

[2] Spillar RJ. Park-and-ride planning and design guidelines, 1997, New York: Parsons Brinckerhoff Incorporated

[3] Currie G, Shalaby A. Synthesis of transport planning approaches for the world’s largest events. Transp Rev, 2012, 32(1): 113-136

[4] Gao Z, Qu Y, Li X, Long J, Huang HJ. Simulating the dynamic escape process in large public places. Oper Res, 2014, 62(6): 1344-1357

[5] Latoski SP, Dunn Jr, WM, Wagenblast B, Randall J, Walker MD (2003) Managing travel for planned special events (No. FHWA-OP-04-010)

[6] Bovy HPPH (2007) Transport and mobility management challenges for the world largest mega-event: 1992 to 2012 Summer Olympic Games. Paper submitted to the UC Berkeley Institute of Transportation Studies, Berkeley, 7, 2007

[7] Meek S, Ison S, Enoch Marcus. Role of bus-based park and ride in the UK: a temporal and evaluative review. Transp Rev, 2008, 28(6): 781-803

[8] Shoup DC. The high cost of free parking, 2005, Washington, DC: Planners Press, American Planning Association

[9] Arnott R, Rowse J. Modeling parking. J Urban Econ, 1999, 45(1): 97-124

[10] Verhoef E, Nijkamp P, Rietveld P. The economics of regulatory parking policies: the (im) possibilities of parking policies in traffic regulation. Transp Res Part A, 1995, 29(2): 141-156.

[11] Fosgerau M, De Palma A. The dynamics of urban traffic congestion and the price of parking. J Public Econ, 2013, 105: 106-115

[12] Liu W, Yang H, Yin Y. Expirable parking reservations for managing morning commute with parking space constraints. Transp Res Part C, 2014, 44: 185-201

[13] He F, Yin Y, Chen Z Pricing of parking games with atomic players. Transp Res Part B, 2015, 73: 1-12

[14] Qian ZS, Rajagopal R. Optimal dynamic parking pricing for morning commute considering expected cruising time. Transp Res Part C, 2014, 48: 468-490

[15] Guo W, Zhang Y, Xu M et al (2016) Parking spaces repurchase strategy design via simulation optimization. J Intell Transp Syst 20(3):255–269

[16] Zheng N, Geroliminis N. Modeling and optimization of multimodal urban networks with limited parking and dynamic pricing. Transp Res Part B, 2016, 83: 36-58

[17] Viswanath K, Peeta S. Multicommodity maximal covering network design problem for planning critical routes for earthquake response. Transp Res Rec, 1857, 2003: 1-10.

[18] Szeto WY, Jiang Y, Wang DZW, Sumalee A. A sustainable road network design problem with land use transportation interaction over time. Netw Spat Econ, 2015, 15(3): 791-822

[19] Cipriani E, Gori S, Petrelli M. Transit network design: a procedure and an application to a large urban area. Transp Res Part C, 2012, 20(1): 3-14

[20] Brands T, van Berkum EC. Performance of a genetic algorithm for solving the multi-objective, multimodal transportation network design problem. Int J Transp, 2014, 2(1): 1-20

[21] Contrerasa I, Fernández E. General network design: a unified view of combined location and network design problems. Eur J Oper Res, 2012, 219(3): 680-697

[22] Miandoabchi E, Daneshzand F, Farahani RZ, Szeto WY. Time-dependent discrete road network design with both tactical and strategic decisions. J Oper Res Soc, 2015, 66: 894-913

[23] Wang JY, Yang H, Lindsey R. Locating and pricing park-and-ride facilities in a linear monocentric city with deterministic mode choice. Transp Res Part B, 2004, 38(8): 709-731

[24] Liu TL, Huang HJ, Yang H, Zhang X. Continuum modeling of park-and-ride services in a linear monocentric city with deterministic mode choice. Transp Res Part B, 2009, 43(6): 692-707

[25] Wang H, Meng Q, Zhang XN. Park-and-ride network equilibrium with heterogeneous commuters and parking space constraint. Transp Res Rec, 2014, 2466: 87-97

[26] Fan W, Khan MB, Ma J Bilevel programming model for locating park-and-ride facilities. J Urban Plan Dev, 2013, 140(3): 04014007

[27] Lai X, Schonfeld P (2016) Concurrent optimization of rail transit alignments and station locations. Urban Rail Transit 2(1):1–15

[28] Abdelghany K, Mahmassani H. Dynamic trip assignment-simulation model for intermodal transportation networks. Transp Res Rec, 2001, 1771: 52-60

[29] Abdelghany KF, Mahmassani HS, Abdelghany AF. A modeling framework for bus rapid transit operations evaluation and service planning. Transp Plan Technol, 2007, 30(6): 571-591

[30] Chang E, Floros E, Ziliaskopoulos A. An intermodal time-dependent minimum cost path algorithm. Dynamic fleet management, 2007, Berlin: Springer 113-132.

[31] Zhang K, Mahmassani H, Chung-Cheng L. Probit-based time-dependent stochastic user equilibrium traffic assignment model. Transp Res Rec, 2008, 2085: 86-94

[32] Zhou X, Mahmassani HS, Zhang K. Dynamic micro-assignment modeling approach for integrated multimodal urban corridor management. Transp Res Part C, 2008, 16(2): 167-186

[33] Mishra S, Welch TF, Torrens PM A tool for measuring and visualizing connectivity of transit stop, route and transfer center in a multimodal transportation network. Public Transp, 2015, 7(1): 77-99

[34] Mahmassani H, Zhang K, Dong J Dynamic network simulation-assignment platform for multiproduct intermodal freight transportation analysis. Transp Res Rec, 2007, 2032: 9-16

[35] Oliveros MM, Nagel K (2011) Automatic calibration of microscopic, activity based demand for a public transit line, Annual Meeting Preprint, pp 12–3279

[36] Rieser M. Adding transit to an agent-based transportation simulation, 2010, Zürich: Swiss Federal Institute of Technology

[37] Pekel E, Kara SS (2015) A simulation based scheduling in bus rapid transit system. Sigma J Eng Nat Sci 33(1):66–76

[38] Yu L, Yu L, Chen X Calibration of VISSIM for bus rapid transit systems in Beijing using GPS data. J Public Transp, 2006, 9(3): 13

[39] Maciejewski M, Nagel K. Towards multi-agent simulation of the dynamic vehicle routing problem in MATSim, parallel processing and applied mathematics, 2011, Berlin: Springer 551-560.

[40] Zhou X, Taylor J. DTALite: a queue-based mesoscopic traffic simulator for fast model evaluation and calibration. Cogent Eng, 2014, 1(1): 961345

[41] Zawack DJ, Thompson GL. A dynamic space–time network flow model for city traffic congestion. Transp Sci, 1987, 21(3): 153-162

[42] Miller HJ. Modelling accessibility using space–time prism concepts within geographical information systems. Int J Geogr Inf Syst, 1991, 5(3): 287-301

[43] Miller HJ. Measuring space–time accessibility benefits within transportation networks: basic theory and computational procedures. Geogr Anal, 1999, 31(1): 1-26

[44] Handy SL (2005) Planning for accessibility: in theory and in practice. In: Levinson DM, Krizek KJ (eds) Access to destinations. Elsevier, Amsterdam, p 131–147

[45] Santos B, Antunes A, Miller EJ (2011) Integrating equity objectives in a road network design model. Transportation Research Record: Journal of the Transportation Research Board, 2089, 2008. Litman, T. Evaluating accessibility for transportation planning. Victoria Transport Policy Institute, Victoria, pp 35–42

[46] Kang JE, Chow JYJ, Recker WW. On activity-based network design problems. Transp Res Part B, 2013, 57: 398-418

[47] Shah N, Kumar S, Bastani K, Yen I-L (2008) A space–time network optimization model for traffic coordination and its evaluation, sensor networks, Ubiquitous and trustworthy computing, pp 177–184

[48] Zhang J, Jia L, Niu S, Zhang F, Tong L, Zhou X. A space–time network-based modeling framework for dynamic unmanned aerial vehicle routing in traffic incident monitoring applications. Sensors, 2015, 15: 13874-13898

[49] Tong L, Zhou X, Miller HJ. Transportation network design for maximizing space–time accessibility. Transp Res Part B, 2015, 81: 555-576

[50] Mahmoudi M, Zhou X. Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: a dynamic programming approach based on state–space–time network representations. Transp Res Part B, 2016, 89: 19-42

Funding

National Science Foundation under grant(CMMI-1538569)