TECHNICAL PROGRAM Wednesday, 9:00-10:30 Wednesday, 11:00-12:40 WA-01 WB-01 Wednesday, 9:00-10:30 - 1a. Europe a Wednesday, 11:00-12:40 - 1a. Europe a Opening session - Plenary I. Ljubic Planning and Operating Metropolitan Passenger Transport Networks Stream: Plenaries and Semi-Plenaries Invited session Stream: Traffic, Mobility and Passenger Transportation Chair: Bernard Fortz Invited session Chair: Oded Cats 1 - From Game Theory to Graph Theory: A Bilevel Jour- ney 1 - Frequency and Vehicle Capacity Determination using Ivana Ljubic a Dynamic Transit Assignment Model In bilevel optimization there are two decision makers, commonly de- Oded Cats noted as the leader and the follower, and decisions are made in a hier- The determination of frequencies and vehicle capacities is a crucial archical manner: the leader makes the first move, and then the follower tactical decision when planning public transport services. All meth- reacts optimally to the leader’s action. It is assumed that the leader can ods developed so far use static assignment approaches which assume anticipate the decisions of the follower, hence the leader optimization average and perfectly reliable supply conditions. The objective of this task is a nested optimization problem that takes into consideration the study is to determine frequency and vehicle capacity at the network- follower’s response. level while accounting for the impact of service variations on users In this talk we focus on new branch-and-cut (B&C) algorithms for and operator costs. To this end, we propose a simulation-based op- dealing with mixed-integer bilevel linear programs (MIBLPs). We first timization approach. Model formulation allows for minimizing user address a general case in which intersection cuts are used to cut off in- costs, operational costs or the combination of which by using simu- feasible solutions. We then focus on a subfamily of MIBLPs in which lated annealing as the search method in combination with a dynamic the leader and the follower share a set of items, and the leader can transit assignment simulation model. select some of the items to inhibit their usage by the follower. In- The iterative model framework consists of three modules: terdiction Problems, Blocker Problems, Critical Node/Edge Detection Problems are some examples of optimization problems that satisfy the (i) a dynamic public transport operations and assignment tool, Bus- later condition. We show that, in case the follower subproblem satisfies Mezzo, that considers the interaction between demand and supply and monotonicity property, a family of "interdiction-cuts" can be derived its potential impacts on service reliability. The assignment model in- resulting in a more efficient B&C scheme. volves an iterative network loading procedure which yield network- wide steady-state conditions which can be seen as an equivalent to the These new B&C algorithms consistently outperform (often by a large congested user equilibrium in conventional static assignment models. margin) alternative state-of-the-art methods from the literature, includ- The model captured the following congestion effects: (1) Deteriorating ing methods that exploit problem specific information for special in- comfort on-board a crowded vehicle, (2) denied boarding in case of in- stance classes. sufficient vehicle capacity, (3) service headway fluctuations resulting from riding and dwell time variations; (ii) evaluating the performance of alternative solutions by transforming the outputs of the assignment model into a transport user and operator cost function. The former is based on value of time coefficients for each passenger travel time component and the latter consists of fixed and variable costs, and; (iii) a search algorithm that selects potential solutions using the meta- heuristic of simulated annealing, a probabilistic metaheuristic to find the global optimum in large search spaces. A neighbour of a specific solution is generated by altering either the headway or the vehicle ca- pacity of a selected line while keeping all other variables unchanged and satisfying the feasibility constraints. The overall model allows accounting for variations in service relia- bility and crowding that have not been accounted for in the tactical planning insofar. Practical benefits of the model are demonstrated by an application to a bus network in the Amsterdam metropolitan area for different demand periods, flexibility in decision variable settings and optimization objectives. Results indicate that the current situa- tion in the regarded network can be improved by changing the supply provision in terms of frequencies and vehicle capacities. This study contributes to the development of a new generation of methods that integrate reliability into the tactical planning phase. 2 - Controlling the propagation of passenger disruption impacts in multi-level public transport networks Menno Yap, Oded Cats Relevance The passenger impact of a disruption on the train network can propagate over the multi-level public transport (PT) network, via the transfer hub to the urban PT network. Hence, an optimal hold- ing control decision for urban services at the transfer location should account for the impact of a disruption on another PT network level. Modelling framework We first quantify the passenger impacts of dis- ruption propagation resulting from an exogenous train network disrup- tion to the urban PT network level. Thereafter, we develop a rule- based controller for holding urban PT services while taking into ac- count predicted passenger delays and rerouting from the train network 1 WB-02 OR2018 – Brussels level caused by the train network disruption. This means that in this 4 - Bus operation modeling to compare conventional study a control decision is triggered by services which are not subject and semi-autonomous buses in serving flexible de- to this same control decision. mand Scenario design We quantify the total passenger welfare for three dif- Wei Zhang, Erik Jenelius, Hugo Badia ferent scenarios, expressed as the generalized travel time over all pas- sengers: -Scenario 1: undisrupted train network; no urban control in- The development of autonomous driving technology potentially en- tervention; -Scenario 2: train network disruption; no urban control in- ables a better public transport service. While there are a bunch of stud- tervention; -Scenario 3: train network disruption; urban control inter- ies about the implementation of fully autonomous (Level-5 automa- vention. tion) taxies and trials on driverless buses, the applicability of semi- Control problem description The applied control strategy entails the autonomous buses has not been adequately explored yet. In our study, decision whether to hold urban PT runs at multi-level transfer stops for semi-autonomous buses belong to the Level-4 automation, and bene- a certain holding time in case a disruption occurs on the train network. fit from labor saving when they form bus platoon(s). The generalized The predicted welfare impacts on four different passenger segments cost is modeled as the sum of waiting cost, riding cost, operating cost are incorporated in this holding decision: (i) Upstream boarding and and capital cost. The discomfort factor is considered in the value of downstream alighting (through) passengers; (ii) Downstream boarding in-vehicle time. Due to reasons such as road geometry and techni- passengers; (iii) Reverse downstream boarding passengers; (iv) Trans- cal concerns, the size (seats plus standees) of buses is restricted to a ferring passengers at holding location. certain threshold. Thus, the problem is formulated as a constrained op- timization problem, where the objective is to minimize the generalized A passenger-oriented decision rule is applied for the controller, where cost and the decision variables are the bus size and the service head- predicted costs of the control decision are deducted from the predicted way. The difference between conventional buses and semi-autonomous control benefits for all passenger segments, aimed at minimizing pas- buses lies in the capital cost and operating cost. It is assumed that senger travel costs on the urban network. semi-autonomous buses cost more than conventional buses for the re- Holding results in a direct extension of in-vehicle time at the hold- quirement of extra modules (e.g., controllers and sensors), while semi- ing stop of passengers who board upstream the holding location and autonomous bus platoons experience a reduction of drivers’ cost in the alight downstream the holding location, and a waiting time extension platoon followers. For conventional bus service, only headways are for downstream boarding passengers, corrected for turnaround buffer adjusted during peak and off-peak hours, whereas semi-autonomous time for reverse downstream boarding passengers. Besides, holding re- bus service can also change the capacity (by adding more buses to the duces waiting time for passengers transferring at the holding location, platoon) without introducing extraordinary additional operating cost. compared to having to wait for the next service. The holding strategy The calculation result shows that the performance of semi-autonomous also affects the different passenger segments in terms of perceived in- buses relies on several parameters. The main aspect is the demand, vehicle time due to changed crowding levels. Due to the non-linear which includes the peak/off-peak demand levels and the relative length nature of perceived in-vehicle time as function of