Schedule-Constrained Demand Management in Two-Region Urban Networks

References

• Aalipour A, Kebriaei H, Ramezani M (2019) Analytical optimal solution of perimeter traffic flow control based on MFD dynamics: A pontryagin’s maximum principle approach. IEEE Trans. Intelligent Transportation Systems 20(9):3224–3234.Crossref, Google Scholar
• Agatz N, Erera A, Savelsbergh M, Wang X (2012) Optimization for dynamic ride-sharing: A review. Eur. J. Oper. Res. 223(2):295–303.Crossref, Google Scholar
• Alfa AS (1986) A review of models for the temporal distribution of peak traffic demand. Transportation Res. Part B 20(6):491–499.Crossref, Google Scholar
• Amirgholy M, Gao HO (2017) Modeling the dynamics of congestion in large urban networks using the macroscopic fundamental diagram: User equilibrium, system optimum, and pricing strategies. Transportation Res. Part B Methodological 104:215–237.Crossref, Google Scholar
• Andersson J (2013) A general-purpose software framework for dynamic optimization. Unpublished doctoral dissertation, Faculty of Engineering Science, KU Leuven, Heverlee, Belgium, https://lirias.kuleuven.be/handle/123456789/418048.Google Scholar
• Arnott R (2013) A bathtub model of downtown traffic congestion. J. Urban Econom. 76:110–121.Crossref, Google Scholar
• Batista SFA, Leclercq L (2019) Regional dynamic traffic assignment framework for macroscopic fundamental diagram multi-regions models. Transportation Sci. 53(6):1563–1590.Link, Google Scholar
• Batista SFA, Leclercq L, Geroliminis N (2019) Estimation of regional trip length distributions for the calibration of the aggregated network traffic models. Transportation Res. Part B Methodological 122:192–217.Crossref, Google Scholar
• Bebber DP, Hynes J, Darrah PR, Boddy L, Fricker MD (2007) Biological solutions to transport network design. Proc. Royal Soc. B Biological Sci. 274(1623):2307–2315.Crossref, Google Scholar
• Boscoe FP, Henry KA, Zdeb MS (2012) A nationwide comparison of driving distance vs. straight-line distance to hospitals. Professional Geographer 64(2):188–196.Crossref, Google Scholar
• Buisson C, Ladier C (2009) Exploring the impact of homogeneity of traffic measurements on the existence of macroscopic fundamental diagrams. Transportation Res. Record 2124(1):127–136.Crossref, Google Scholar
• Cantarella GE, Cascetta E (1995) Dynamic processes and equilibrium in transportation networks: Toward a unifying theory. Transportation Sci. 29(4):305–329.Link, Google Scholar
• Cao J, Menendez M, Waraich R (2019) Impacts of the urban parking system on cruising traffic and policy development: The case of Zurich downtown area, Switzerland. Transportation 46:883–908.Crossref, Google Scholar
• Cardillo A, Scellato S, Latora V, Porta S (2006) Structural properties of planar graphs of urban street patterns. Physical Rev. E 73:66107.Crossref, Google Scholar
• Cassidy MJ, Jang K, Daganzo CF (2011) Macroscopic fundamental diagrams for freeway networks: Theory and observation. Transportation Res. Record 2260(1):8–15.Crossref, Google Scholar
• Chen R, Mahmassani HS (2004) Travel time perception and learning mechanisms in traffic networks. Transportation Res. Record 1894(1):209–221.Crossref, Google Scholar
• Chen PST, Srinivasan KK, Mahmassani HS (1999) Effect of information quality on compliance behavior of commuters under real-time traffic information. Transportation Res. Record 1676(1):53–60.Crossref, Google Scholar
• Chiu YC (2014) Active traffic and demand management system. U.S. Patent 8744734B2, https://patents.google.com/patent/US8744734B2.Google Scholar
• Cole JP, King CAM (1968) Quantitative Geography: Techniques and Theories in Geography (Wiley, New York).Google Scholar
• Daganzo CF (1985) The uniqueness of a time-dependent equilibrium distribution of arrivals at a single bottleneck. Transportation Sci. 19(1):29–37.Link, Google Scholar
• Daganzo CF (2007) Urban gridlock: Macroscopic modeling and mitigation approaches. Transportation Res. Part B Methodological 41(1):49–62.Crossref, Google Scholar
• Daganzo CF, Lehe LJ (2015) Distance-dependent congestion pricing for downtown zones. Transportation Res. Part B Methodological 75:89–99.Crossref, Google Scholar
• De Palma A, Lindsey R (2011) Traffic congestion pricing methodologies and technologies. Transportation Res. Part C Emerging Tech. 19(6):1377–1399.Crossref, Google Scholar
• De Palma A, Ben-akiva M, Lefèvre C, Litinas N (1983) Stochastic equilibrium model of peak period traffic congestion. Transportation Sci. 17(4):430–453.Link, Google Scholar
• Fosgerau M (2015) Congestion in the bathtub. Econom. Transportation 4(4):241–255.Crossref, Google Scholar
• Fosgerau M, Small KA (2013) Hypercongestion in downtown metropolis. J. Urban Econom. 76:122–134.Crossref, Google Scholar
• Fox JE, Boehm-Davis DA (1998) Effects of age and congestion information accuracy of advanced traveler information systems on user trust and compliance. Transportation Res. Record 1621(1):43–49.Crossref, Google Scholar
• Geroliminis N, Daganzo CF (2008) Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings. Transportation Res. Part B Methodological 42(9):759–770.Crossref, Google Scholar
• Geroliminis N, Levinson DM (2009) Cordon pricing consistent with the physics of overcrowding. Lam WHK, Wong SC, Lo HK, eds. Transportation and Traffic Theory 2009: Golden Jubilee (Springer, Boston), 219–240.Crossref, Google Scholar
• Geroliminis N, Sun J (2011) Properties of a well-defined macroscopic fundamental diagram for urban traffic. Transportation Res. Part B Methodological 45(3):605–617.Crossref, Google Scholar
• Geroliminis N, Zheng N, Ampountolas K (2014) A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks. Transportation Res. Part C Emerging Tech. 42:168–181.Crossref, Google Scholar
• Godfrey JW (1969) The mechanism of a road network. Traffic Engrg. Control 11:323–327.Google Scholar
• González Ramírez H, Leclercq L, Chiabaut N, Becarie C, Krug J (2019) Unravelling travellers’ route choice behaviour at full-scale urban network by focusing on representative OD pairs in computer experiments. PloS one 14(11):e0225069.Crossref, Google Scholar
• Gu Z, Shafiei S, Liu Z, Saberi M (2018) Optimal distance- and time-dependent area-based pricing with the network fundamental diagram. Transportation Res. Part C Emerging Tech. 95:1–28.Crossref, Google Scholar
• Guo RY, Yang H, Huang HJ, Li X (2018) Day-to-day departure time choice under bounded rationality in the bottleneck model. Transportation Res. Part B Methodological 117:832–849.Crossref, Google Scholar
• Hendrickson C, Kocur G (1981) Schedule delay and departure time decisions in a deterministic model. Transportation Sci. 15(1):62–77.Link, Google Scholar
• Herman R, Prigogine I (1979) A two-fluid approach to town traffic. Science 204(4389):148–151.Crossref, Google Scholar
• Horowitz JL (1984) The stability of stochastic equilibrium in a two-link transportation network. Transportation Res. Part B Methodological 18(1):13–28.Crossref, Google Scholar
• Huang L, Li J (2011) Applicability of staggered work hours for urban traffic: Case of Guangzhou. Proc. 3rd Internat. Conf. Transportation Engrg. (American Society of Civil Engineers, Reston, VA), 404–409.Google Scholar
• Ingole D, Mariotte G, Leclercq L (2020) Perimeter gating control and citywide dynamic user equilibrium: a macroscopic modeling framework. Transportation Res. Part C Emerging Tech. 111:22–49.Crossref, Google Scholar
• Jahn O, Möhring RH, Schulz AS, Stier-Moses NE (2005) System-optimal routing of traffic flows with user constraints in networks with congestion. Oper. Res. 53(4):600–616.Link, Google Scholar
• Lamotte R, Geroliminis N (2018) The morning commute in urban areas with heterogeneous trip lengths. Transportation Res. Part B Methodological 117:794–810.Crossref, Google Scholar
• Leclercq L, Paipuri M (2019) Macroscopic traffic dynamics under fast-varying demand. Transportation Sci. 53(6):1526–1545.Link, Google Scholar
• Leclercq L, Parzani C, Knoop VL, Amourette J, Hoogendoorn SP (2015) Macroscopic traffic dynamics with heterogeneous route patterns. Transportation Res. Part C Emerging Tech. 59:292–307.Crossref, Google Scholar
• Lindsney R, Verhoef E (2001) Traffic congestion and congestion pricing. Button KJ, Hensher DA, eds. Handbook of Transport Systems and Traffic Control (Emerald, Bingley, UK), 77–105.Crossref, Google Scholar
• Little JDC (1961) A proof for the queuing formula: L = λW. Oper. Res. 9(3):383–387.Link, Google Scholar
• Liu W, Geroliminis N (2016) Modeling the morning commute for urban networks with cruising-for-parking: An MFD approach. Transportation Res. Part B Methodological 93:470–494.Crossref, Google Scholar
• Loder A, Ambühl L, Menendez M, Axhausen KW (2017) Empirics of multi-modal traffic networks—using the 3D macroscopic fundamental diagram. Transportation Res. Part C Emerging Tech. 82:88–101.Crossref, Google Scholar
• Mahmassani H, Williams JC, Herman R (1984) Investigation of network-level traffic flow relationships: Some simulation results. Transportation Res. Record (971):121–130.Google Scholar
• Marchand M (1968) A note on optimal tolls in an imperfect environment. Econometrica 36(3–4):575–581.Crossref, Google Scholar
• Mariotte G, Leclercq L (2019) Flow exchanges in multi-reservoir systems with spillbacks. Transportation Res. Part B Methodological 122:327–349.Crossref, Google Scholar
• Mariotte G, Leclercq L, Laval JA (2017) Macroscopic urban dynamics: Analytical and numerical comparisons of existing models. Transportation Res. Part B Methodological 101:245–267.Crossref, Google Scholar
• Mohajerpoor R, Saberi M, Vu HL, Garoni TM, Ramezani M (2019) H∞ robust perimeter flow control in urban networks with partial information feedback. Transportation Res. Part B Methodological 137:47–73.Crossref, Google Scholar
• Nakayama S, Kitamura R, Fujii S (1999) Drivers’ learning and network behavior: Dynamic analysis of the driver-network system as a complex system. Transportation Res. Record 1676(1):30–36.Crossref, Google Scholar
• Newell GF (1987) The morning commute for nonidentical travelers. Transportation Sci. 21(2):74–88.Link, Google Scholar
• NYC OpenData (2018) New York City taxi trip data 2009–2015, https://opendata.cityofnewyork.us/.Google Scholar
• O’Flaherty C (1997) Transport Administration and Planning (Wiley, New York).Crossref, Google Scholar
• Orski CK (1990) Can management of transportation demand help solve our growing traffic congestion and air pollution problems? Transportation Quart. 44:483–498.Google Scholar
• Paipuri M, Xu Y, González MC, Leclercq L (2020) Estimating MFDs, trip lengths and path flow distributions in a multi-region setting using mobile phone data. Transportation Res. Part C Emerging Tech. 118:102709.Crossref, Google Scholar
• Papageorgiou M (2004) Overview of road traffic control strategies. IFAC Proc. 37(19):29–40.Crossref, Google Scholar
• Peeta S, Ziliaskopoulos AK (2001) Foundations of dynamic traffic assignment: The past, the present and the future. Networks Spatial Econom. 1(3):233–265.Crossref, Google Scholar
• Ramezani M, Nourinejad M (2018) Dynamic modeling and control of taxi services in large-scale urban networks: A macroscopic approach. Transportation Res. Part C Emerging Tech. 94:203–219.Crossref, Google Scholar
• Ran B, Hall RW, Boyce DE (1996) A link-based variational inequality model for dynamic departure time/route choice. Transportation Res. Part B Methodological 30(1):31–46.Crossref, Google Scholar
• Saffari E, Yildirimoglu M, Hickman M (2020) A methodology for identifying critical links and estimating macroscopic fundamental diagram in large-scale urban networks. Transportation Res. Part C Emerging Tech. 119:102743.Crossref, Google Scholar
• Sheffi Y (1984) Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods (Prentice-Hall, Englewood Cliffs, NJ).Google Scholar
• Shoup DC (1997) The high cost of free parking. J. Planning Education Res. 17(1):3–20.Crossref, Google Scholar
• Sirmatel II, Geroliminis N (2018) Economic model predictive control of large-scale urban road networks via perimeter control and regional route guidance. IEEE Trans. Intelligent Transportation Systems 19(4):1112–1121.Crossref, Google Scholar
• Small KA, Chu X (2003) Hypercongestion. J. Transport Econom. Policy 37(3):319–352.Google Scholar
• Smeed RJ (1966) The road capacity of city centers. Highway Res. Record 169:22–29.Google Scholar
• Srinivasan KK, Mahmassani HS (2000) Modeling Inertia and Compliance Mechanisms in Route Choice Behavior Under Real-Time Information. Transportation Res. Record 1725(1):45–53.Crossref, Google Scholar
• Thomson JM (1967) An evaluation of two proposals for traffic restraint in central London. J. Royal Statist. Soc. Ser. A 130(3):327–377.Crossref, Google Scholar
• Vickrey WS (1969) Congestion theory and transport investment. Amer. Econom. Rev. 59(2):251–260.Google Scholar
• Wächter A, Biegler LT (2006) On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math. Programming 106(1):25–57.Crossref, Google Scholar
• Wardman M (2004) Public transport values of time. Transport Policy 11:363–377.Crossref, Google Scholar
• Wardrop JG (1952) Some theoretical aspects of road traffic research. Proc. Institution Civil Engineers 1(5):767–768.Crossref, Google Scholar
• Wardrop JG (1968) Journey speed and flow in central urban areas. Traffic Engrg. Control 9:528–532.Google Scholar
• Yang H (2005) Mathematical and Economic Theory of Road Pricing (Emerald, Bingley, UK).Crossref, Google Scholar
• Yang H, Ke J, Ye J (2018) A universal distribution law of network detour ratios. Transportation Res. Part C Emerging Tech. 96:22–37.Crossref, Google Scholar
• Yang K, Menendez M, Zheng N (2019) Heterogeneity aware urban traffic control in a connected vehicle environment: A joint framework for congestion pricing and perimeter control. Transportation Res. Part C Emerging Tech. 105:439–455.Crossref, Google Scholar
• Yang K, Zheng N, Menendez M (2018) Multi-scale perimeter control approach in a connected-vehicle environment. Transportation Res. Part C Emerging Tech. 94:32–49.Crossref, Google Scholar
• Yildirimoglu M, Geroliminis N (2014) Approximating dynamic equilibrium conditions with macroscopic fundamental diagrams. Transportation Res. Part B Methodological 70:186–200.Crossref, Google Scholar
• Yildirimoglu M, Kahraman O (2018) Searching for empirical evidence on traffic equilibrium. PloS One 13(5):e0196997.Crossref, Google Scholar
• Yildirimoglu M, Ramezani M (2020) Demand management with limited cooperation among travellers: A doubly dynamic approach. Transportation Res. Part B Methodological 132:267–284.Crossref, Google Scholar
• Yildirimoglu M, Ramezani M, Geroliminis N (2015) Equilibrium analysis and route guidance in large-scale networks with MFD dynamics. Transportation Res. Part C Emerging Tech. 59:404–420.Crossref, Google Scholar
• Yildirimoglu M, Sirmatel II, Geroliminis N (2018) Hierarchical control of heterogeneous large-scale urban road networks via path assignment and regional route guidance. Transportation Res. Part B Methodological 118:106–123.Crossref, Google Scholar
• Yin YY, Yang H (2003) Simultaneous determination of the equilibrium market penetration and compliance rate of advanced traveler information systems. Transportation Res. Part A Policy Practice 37(2):165–181.Crossref, Google Scholar
• Yu H (2008) The geography of transport systems. Professional Geographer 60:435–437.Crossref, Google Scholar
• Zahavi Y (1972) Traffic performance evaluation of road networks by the alpha-relationship: Part 1. Traffic Engrg. Control 14:228–231.Google Scholar
• Zhang X, Adamatzky A, Chan FTS, Deng Y, Yang H, Yang XS, Tsompanas MAI, Sirakoulis GC, Mahadevan S (2015) A biologically inspired network design model. Sci. Rep. 5:10794.Crossref, Google Scholar
• Zheng N, Geroliminis N (2016) Modeling and optimization of multimodal urban networks with limited parking and dynamic pricing. Transportation Res. Part B: Methodological 83:36–58.Crossref, Google Scholar
• Zheng N, Geroliminis N (2020) Area-based equitable pricing strategies for multimodal urban networks with heterogeneous users. Transportation Res. Part A Policy Practice 136:357–374.Crossref, Google Scholar