Abstract

As the products of intelligent transportation systems, parking apps have become convenient platforms for implementing parking policies, which can be provided as parking app services. This paper proposes a traffic simulation model for evaluating the impacts of parking app services on the travellers’ choice behaviour and traffic dynamics. Travellers are assumed to use three types of parking app services: the provision of information on real-time parking lot occupancies, parking reservation, and the display of dynamic parking fees. The behaviour of travellers, such as travellers’ mode choices, departure time choices, and learning behaviour, are considered in this model. Numerical experiments show that providing information on real-time parking lot occupancies can be helpful in reducing the use ratio of commercial parking lots, but the effect will ultimately be smoothed during the evolution of traffic dynamics. Moreover, parking reservation is an effective way to reduce travel costs and encourage travellers to choose park-and-ride. Furthermore, dynamic parking fees usually lead to the oscillation of traffic dynamics and travellers’ choices, in addition to an increase in travel costs. This model is a useful tool for analysing the impacts of other parking management policies that can be implemented as parking app services and can be a reference for evaluating the impacts of other parking polices.

References

Vickrey WS. Congestion Theory and Transport Investment. American Economic Review. 1969;59(2): 251-60. Available from: https://www.jstor.org/stable/1823678

Ibeas A, Cordera R, Dell'Olio L, Moura JL. Modelling demand in restricted parking zones. Transportation Research Part A: Policy & Practice. 2011;45(6): 485-98. Available from: doi:10.1016/j.tra.2011.03.004

Sheffi Y. Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. Prentice-Hall; 1984.

Szeto WY, Wong SC. Dynamic traffic assignment: model classifications and recent advances in travel choice principles. Central European Journal of Engineering. 2012;2(1): 1-18. Available from: doi:10.2478/s13531-011-0057-y

Caicedo F. Real-time parking information management to reduce search time, vehicle displacement and emissions. Transportation Research Part D. 2010;15(4): 228-34. Available from: doi:10.1016/j.trd.2010.02.008

Yang J, Li X, Liu S. A Reservation Strategy Model of Finite Berth Based on Active Parking Guidance and Information System. Information Security and Assurance. 2009: 1-4. Available from: https://ieeexplore.ieee.org/document/5072670

Liang JJ, Zhang XN. A Simulation Model for Traffic Mode Choice under the Provision of Real-time Parking Lot's Information. Journal of Transportation Systems Engineering & Information Technology. 2018;18(2): 52-9. Available from: http://www.tseit.org.cn/CN/Y2018/V18/I2/52

Kaspi M, Raviv T, Tzur M. Parking reservation policies in one-way vehicle sharing systems. Transportation Research Part B. 2014;62(2): 35-50. Available from: doi:10.1016/j.trb.2014.01.006

Qian Z, Rajagopal R. Optimal dynamic pricing for morning commute parking. Transportmetrica. 2015;11(4): 291-316. Available from: doi:10.1080/23249935.2014.986671

Chaniotakis E, Pel AJ. Drivers' parking location choice under uncertain parking availability and search times: A stated preference experiment. Transportation Research Part A. 2015;82: 228-39. Available from: doi:10.1016/

j.tra.2015.10.004

Mahmassani HS, Liu YH. Dynamics of commuting decision behaviour under advanced traveller information systems. Transportation Research Part C: Emerging Technologies. 1999;7: 91-107. Available from: doi:10.1016/S0968-090X(99)00014-5

Liu Y. Comparative Study of the Effects od Auditory, Visual and Multimodality Displays on Drivers' performance in Advanced Traveller Information Systems. Ergonomics. 2001;44(4): 425-42. Available from: doi:10.1080/00140130010011369

Dingus TA, Hulse MC, Mollenhauer MA, Fleischman RN, Mcgehee DV, Manakkal N. Effects of age, system experience, and navigation technique on driving with an advanced traveler information system. Human Factors. 1997;39(2): 177-99. Available from: doi:10.1518/001872097778543804

Nie X, Zhang HM. A Comparative Study of Some Macroscopic Link Models Used in Dynamic Traffic Assignment. Networks & Spatial Economics. 2005;5(1): 89-115. Available from: doi:10.1007/s11067-005-6663-6

Liu W, Geroliminis N. Doubly dynamics for multi-modal networks with park-and-ride and adaptive pricing. Transportation Research Part B: Methodological. 2017;102: 162-79. Available from: doi:10.1016/j.trb.2017.05.010

Shen W, Zhang HM. What Do Different Traffic Flow Models Mean for System-Optimal Dynamic Traffic Assignment in a Many-to-One Network? Transportation Research Record, Journal of the Transportation Research Board. 2008;2088(2088): 157-66. Available from: doi:10.3141/2088-17

Zhang X, Yang H, Huang HJ. Improving travel efficiency by parking permits distribution and trading. Transportation Research Part B. 2011;45(7): 1018-34. Available from: doi:10.1016/j.trb.2011.05.003

Wang J, Zhang X, Zhang HM. Parking permits management and optimal parking supply considering traffic emission cost. Transportation Research Part D. 2018. Available from: doi:10.1016/j.trd.2016.02.005

Cantarella GE. Day-to-day dynamic models for Intelligent Transportation Systems design and appraisal. Transportation Research Part C: Emerging Technologies. 2013;29(1): 117-30. Available from: doi:10.1016/j.trc.2012.03.005

Kroese DP, Brereton T, Taimre T, Botev ZI. Why the Monte Carlo method is so important today. Wiley Interdisciplinary Reviews Computational Statistics. 2014;6(6): 386-92. Available from: doi:10.1002/wics.1314

Liu W, Li X, Zhang F, Yang H. Interactive travel choices and traffic forecast in a doubly dynamical system with user inertia and information provision. Transportation Research Part C: Emerging Technologies. 2017;85: 711-31. Available from: doi:10.1016/j.trc.2017.10.021