Abstract
Designing a public transport timetable that maximizes passenger service, measured in weighted travel time, is an intricate problem. The weighted travel time depends on the free route choice of passengers. Passenger route choice depends on the timetable. In turn, the timetable that minimizes weighted travel time depends on the route choice of passengers—and therefore requires passenger route choice information. Consequently, a sequential approach where timetables are designed provided pre-fixed passenger assignment to routes may not find the optimal timetable. This paper aims to integrate passenger route choice and timetabling. It addresses the problem of designing maximal passenger service public transport timetables in systems with free route choice within a budget for operating costs. Operating costs are defined by the minimal cost vehicle schedule required to operate the timetable. The proposed methodology integrates a matheuristic for timetabling and vehicle scheduling with a passenger assignment model in an iterative framework, where different forms of integration are evaluated. Focus is on long- to medium-term timetabling, provided an initial timetable. The results for a realistic case study in the Greater Copenhagen area indicate that our approach consistently leads, at no additional cost, to timetables that represent a reduction in passenger weighted travel time in comparison with both an initial timetable and a non-integrated timetabling method that receives a single-passenger assignment as input.
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Notes
Rapidis is a Danish company that develops tools for planning in Transportation and Logistics. website: http://www.rapidis.com/.
Center for Transport Analytics website: http://www.cta.man.dtu.dk/.
References
Ben-Akiva M, Ramming M, Bekhor S (2004) Route choice models. Human Behaviour and Traffic Networks pp 23–45
Binder S, Maknoon Y, Bierlaire M (2017) The multi-objective railway timetable rescheduling problem. Trans Res Part C: Emerg Technol 78:78–94
Borndörfer R, Hoppmann H, Karbstein M (2017) Passenger routing for periodic timetable optimization. Public Transport 9(1–2):115–135
Briem L, Buck S, Ebhart H, Mallig N, Strasser B, Vortisch P, Wagner D, Zündorf T (2017) Efficient traffic assignment for public transit networks. In: 16th International Symposium on Experimental Algorithms (SEA 2017), vol. 75. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik
Canca D, Barrena E, De-Los-Santos A, Andrade-Pineda JL (2016) Setting lines frequency and capacity in dense railway rapid transit networks with simultaneous passenger assignment. Trans Res Part B: Methodol 93:251–267
Chu JC (2018) Mixed-integer programming model and branch-and-price-and-cut algorithm for urban bus network design and timetabling. Trans Res Part B: Methodol 108:188–216
Comi A, Nuzzolo A, Crisalli U, Rosati L (2017) A new generation of individual real-time transit information systems. In: Nuzzolo A, Lam WH (eds) Modelling intelligent multi-modal transit systems. CRC Press, Taylor & Francis Group, pp 80–107
Dollevoet T, Huisman D (2014) Fast heuristics for delay management with passenger rerouting. Public Transport 6(1–2):67–84
Domencich TA, McFadden D (1975) Urban travel demand - A Behavioral Analysis. North-Holland Publishing co, Ltd
Fonseca J, van der Hurk E, Roberti R, Larsen A (2018) A matheuristic for transfer synchronization through integrated timetabling and vehicle scheduling. Trans Res Part B: Methodol 109:128–149
Gattermann P, Großmann P, Nachtigall K, Schöbel A (2016) Integrating passengers’ routes in periodic timetabling: A sat approach. In: OASIcs-OpenAccess Series in Informatics, vol. 54. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik
Gentile G, Papola A(2006) An Alternative Approach to Route Choice Simulation: The Sequential Models. In: Proceedings of the European Transport Conference. Association for European Transport (2006)
Hartleb J, Schmidt M (2019) Railway timetabling with integrated passenger distribution. Available at SSRN 3505167
Kroon L, Maróti G, Nielsen L (2014) Rescheduling of railway rolling stock with dynamic passenger flows. Transp Sci 49(2):165–184
Liu T, Ceder A (2017) Integrated public transport timetable synchronization and vehicle scheduling with demand assignment: a bi-objective bi-level model using deficit function approach. Transp Res Part B: Methodol 117:935–955
Petersen HL, Larsen A, Madsen OBG, Petersen B, Ropke S (2013) The simultaneous vehicle scheduling and passenger service problem. Transp Sci 47(4):603–616
Polinder GJ, Cacchiani V, Schmidt M, Huisman D (2020) An iterative heuristic for passenger-centric train timetabling with integrated adaption times. Tech. rep., Erasmus University Rotterdam, The Netherlands . http://hdl.handle.net/1765/127816
Robenek T, Azadeh SS, Maknoon Y, de Lapparent M, Bierlaire M (2018) Train timetable design under elastic passenger demand. Transp Res Part B: Methodol 111:19–38
Schiewe P (2020) Integrating timetabling and passenger routing. In: Integrated Optimization in Public Transport Planning, pp 33–64. Springer
Schmidt M (2014) Integrating routing decisions in network problems. Op Res Proc 2012:21–26
Schmidt M, Schöbel A (2015) Timetabling with passenger routing. OR spectrum 37(1):75–97
Van der Hurk E, Kroon L, Maróti G (2018) Passenger advice and rolling stock rescheduling under uncertainty for disruption management. Transp Sci 52(6):1391–1411
Veelenturf LP, Kroon LG, Maróti G (2017) Passenger oriented railway disruption management by adapting timetables and rolling stock schedules. Transp Res Part C: Emerg Technol 80:133–147
Wagenaar J, Kroon L, Fragkos I (2017) Rolling stock rescheduling in passenger railway transportation using dead-heading trips and adjusted passenger demand. Transp Res Part C: Emerg Technol 101:140–161
Wang Y, D’Ariano A, Yin J, Meng L, Tang T, Ning B (2018) Passenger demand oriented train scheduling and rolling stock circulation planning for an urban rail transit line. Transp Res Part C: Emerg Technol 118:193–227
Wu W, Liu R, Jin W, Ma C (2019) Stochastic bus schedule coordination considering demand assignment and rerouting of passengers. Transp Res Part C: Emerg Technol 121:275–303
Zhu Y, Mao B, Bai Y, Chen S (2017) A bi-level model for single-line rail timetable design with consideration of demand and capacity. Transp Res Part C: Emerg Technol 85:211–233
Acknowledgements
The authors would like to thank Movia and Rapidis for providing the necessary data for the case study, and for assistance in processing that data. The authors would also like to express their gratitude to Innovationsfonden Denmark for the financial support of this study.
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Appendices
Appendix 1 - Sets, parameters, and decision variables used in the IT-VSP mathematical model
Sets | |
S | Set of all stops |
L | Set of all directed lines |
\(T=\{1,\dots ,n\}\) | Set of all timetabled trips |
\(T_l \subseteq T\) | Subset of all trips in the directed line \(l \in L\) |
\(T^1 \subseteq T\) | Set of all trips which are the first in their directed line |
\(S_i \subseteq S\) | Set of all stops visited by trip \(i\in T\) |
\(J_i \subseteq S_i\) | Set of all intermediate stops visited by trip \(i \in T\), i.e. \(J_i=S_i\setminus \{st_i, et_i\}\) |
R | Set of all transfer opportunities, each defined by a triplet (i, l, s): passengers disembarking trip \(i \in T\) at stop \(s\in J_i\cup \{et_i\}\) with the intent of embarking a trip \(j\in T_l\) of line \(l\in L\) such that \(l\ne l_i\) and \(s\in J_j\cup \{st_j\}\) |
K | Set of all depots |
I | Set of all compatible trips, \(I=\{(i,j)|i,j \in T:i\ne j, Dist(et_i,st_j)\le u, a_{i,et_i}^{-}+q^{-}+b_{ij}\le d_{j,st_j}^{+},a_{i,et_i}^{+}+q^{+} + b_{ij}\ge d_{j,st_j}^{-} \}\) |
\(V_k\) | Set of nodes, which contains a node for each trip \(i\in T\), as well as for depot \(k \in K\) which is denoted \(n+k\), thus \(V_k=T\cup \{n+k\}\) |
\(A_k\) | Set of arcs, including deadhead trips, pull-out trips, and pull-in trips, thus \(A_k=I\cup (\{n+k\} \times T)\cup (T\times \{n+k\})\) |
\(G_k=(V_k, A_k)\) | Graph associated with depot \(k \in K\) |
\(Q^D\) | Set of all deadhead triplets \(Q^D=\{ (i,j,k) : k \in K, (i,j) \in I\}\) |
\(Q^O\) | Set of all pull-out triplets \(Q^O=\{(n+k,j,k): k \in K, j \in T \}\) |
\(Q^H\) | Set of all pull-in triplets \(Q^H=\{ (i,n+k,k) : i \in T, k \in K \}\) |
Q | Set of all compatible triplets (i, j, k), representing a vehicle from depot \(k\in K\) covering the pair of trips \((i,j)\in A_k\). \(Q = Q^D \cup Q^O \cup Q^H\) |
T(Q) | Set of all pairs of trips \(i,j\in T\) for which a triplet involving i and j exists, \(T(Q)=\{(i,j)|i,j\in T: \exists (i,j,k) \in Q\}\). |
Parameters | |
\(l_i\) | Directed line of trip \(i\in T\) |
\(t_i\) | Total travel time of trip \(i\in T\) in the initial timetable |
\(st_i \in S_i\) | Start terminal of trip \(i\in T\) |
\(et_i \in S_i\) | End terminal of trip \(i\in T\) |
\(h_{is}^-, h_{is}^+\) | Minimum and maximum headways, respectively, in relation to the timetabled headways, for each trip \(i\in T\) at each stop \(s\in J_i\cup \{st_i\}\) |
\(d_{i,st_i}^-, d_{i,st_i}^+\) | Minimum and maximum departure shift from the first station for trip \(i\in T\), defined in relation to its departure time in the initial timetable |
\(w_{is}^-\) | Dwell time in the initial timetable of a trip \(i\in T\) at stop \(s\in J_i\) |
\(w_{is}^+\) | Maximum allowed dwell time of a trip \(i\in T\) at stop \(s\in J_i\) |
w | Upper bound on the total added dwell time to all stops of any trip |
\(\Lambda _{is}\) | Number of passengers that are on board (and will continue on board) when trip \(i\in T\) arrives at stop \(s\in J_i\) |
\(a_{is}^-,a_{is}^+\) | Earliest and latest arrival times of trip \(i\in T\) at stop \(s\in J_i\cup \{et_i\}\), determined by the possible timetable modifications |
\(d_{is}^-,d_{is}^+\) | Earliest and latest departure times of trip \(i\in T\) from stop \(s\in J_i\cup \{st_i\}\), determined by the possible timetable modifications |
\(f_r\) | Number of passengers requesting transfer \(r\in R\) |
\(e_r\) | Minimum transfer time for transfer \(r\in R\) |
\(q^-,q^+\) | Minimum and maximum turnaround times |
\(b_{ij}\) | Driving time between \(et_i\) and \(st_j\) |
\(v_k\) | Number of schedules that can be created departing from depot \(k \in K\) |
Dist(i, j) | Distance between the end terminal of trip \(i \in T\), \(et_i\), and the start terminal of trip \(j\in T\), \(st_j\) |
u | Maximum deadhead distance |
\(c_{ijk}\) | Operating cost associated with servicing triplet \((i,j,k)\in Q\). The cost \(c_{ijk}\) of triplet \((i,j,k)\in Q\) is equal to the deadhead time \(b_{ij}\) multiplied by a driving cost per time unit; if \((i,j,k)\in Q^O\), \(c_{ijk}\) also includes a fixed cost for creating a new schedule, corresponding to the fixed cost for using a vehicle. |
\(c^{DW}\) | Operating cost per minute of extra dwell time |
\(c^{OB}\) | Cost per minute of extra dwell time per on-board passenger |
\(c^{TR}\) | Cost per minute of excess transfer time at transfers per passenger |
B | Budget value for the operating costs |
M | Big M value. In this problem, it is sufficient to set a big M value higher than the number of minutes in the planning horizon |
Decision variables | |
\(x_{ijk} \in \{0,1\}\) | 1 if and only if a vehicle from depot k travels from node i directly to node j, 0 otherwise |
\(\tau ^d_{is} \in {\mathbb {Z}}^+_0\) | Departure time of trip \(i \in T\) from stop \(s \in J_i\cup \{st_i\}\) |
\(\tau ^a_{is} \in {\mathbb {Z}}^+_0\) | Arrival time of trip \(i \in T\) at stop \(s \in J_i\cup \{et_i\}\) |
\(\gamma _r \in {\mathbb {R}}^+_0\) | Excess transfer time for passengers using transfer location \(r\in R\) |
\(\alpha _{ijs} \in \{0,1\}\) | 1 if and only if passengers of transfer location \(r=(i,l_j,s)\in R\) embark trip \(j\in T\), 0 otherwise |
\(\delta _{is} \in {\mathbb {Z}}^+_0\) | Minutes of dwell time added to trip \(i\in T\) at stop \(s \in J_i\) |
Appendix 2 - Histogram of added dwell time in a representative solution
See Fig. 9.
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Fonseca, J.P., Zündorf, T., van der Hurk, E. et al. A matheuristic for passenger service optimization through timetabling with free passenger route choice. OR Spectrum 44, 1087–1129 (2022). https://doi.org/10.1007/s00291-022-00681-0
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DOI: https://doi.org/10.1007/s00291-022-00681-0