TY - JOUR
T1 - Exploring trade-offs in frequency allocation in a transit network using bus route patterns
T2 - Methodology and application to large-scale urban systems
AU - Verbas, I. Ömer
AU - Mahmassani, Hani S.
N1 - Funding Information:
This paper is based on research work supported by the Chicago Transit Authority. The work has benefited from Prof. Joseph Schofer’s and Dr. Hamed Alibabai’s suggestions and feedback. Several researchers at the Northwestern University Transportation Center have assisted this study including Charlotte Frei, who had the main role in providing heterogeneous elasticities and Raymond Chan, who developed a graphical user interface to visualize the results. The authors have benefited from the insightful comments and suggestions of two anonymous referees; the subsection on convexity analysis was added in response to referee’s astute observation. The results and views presented in this paper are solely those of the authors.
Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/11/1
Y1 - 2015/11/1
N2 - Transit agencies seek to allocate their limited operational budget and fleet optimally to service routes in order to maximize user benefits. The Transit Network Frequency Setting Problem formulation developed in this study effectively captures the coupling between the routes and their prevailing patterns, which may have different subsets of stops visited at different times of the day. The number of riders is elastic to the prevailing number of bus trips at a given stop, which is the combination of different pattern dispatch frequencies. As a result, the study bridges the gap between the operator's perspective where the decision unit is the pattern schedule, and the user's perspective, which perceives frequencies at the route level. Two main formulations are introduced. The first one maximizes the number of riders and the total waiting time savings under budget, fleet, policy headway and bus loading constraints; the second minimizes the net cost under fleet, policy headway, bus loading, minimum ridership and minimum waiting time savings constraints. In both formulations, pattern headways are the decision variables. Spatial and temporal heterogeneity of ridership elasticity with respect to headway is captured. The formulations are applied to a large-scale test network for the Chicago area. The results show that a win-win solution is possible where both ridership and waiting time savings are increased, while the net cost is decreased.
AB - Transit agencies seek to allocate their limited operational budget and fleet optimally to service routes in order to maximize user benefits. The Transit Network Frequency Setting Problem formulation developed in this study effectively captures the coupling between the routes and their prevailing patterns, which may have different subsets of stops visited at different times of the day. The number of riders is elastic to the prevailing number of bus trips at a given stop, which is the combination of different pattern dispatch frequencies. As a result, the study bridges the gap between the operator's perspective where the decision unit is the pattern schedule, and the user's perspective, which perceives frequencies at the route level. Two main formulations are introduced. The first one maximizes the number of riders and the total waiting time savings under budget, fleet, policy headway and bus loading constraints; the second minimizes the net cost under fleet, policy headway, bus loading, minimum ridership and minimum waiting time savings constraints. In both formulations, pattern headways are the decision variables. Spatial and temporal heterogeneity of ridership elasticity with respect to headway is captured. The formulations are applied to a large-scale test network for the Chicago area. The results show that a win-win solution is possible where both ridership and waiting time savings are increased, while the net cost is decreased.
KW - Frequency allocation
KW - Frequency setting
KW - Large-scale transit networks
KW - Nonlinear optimization
KW - Spacio-temporally varying demand elasticities
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U2 - 10.1016/j.trb.2015.06.018
DO - 10.1016/j.trb.2015.06.018
M3 - Article
AN - SCOPUS:84947487060
VL - 81
SP - 577
EP - 595
JO - Transportation Research, Series B: Methodological
JF - Transportation Research, Series B: Methodological
SN - 0191-2615
ER -