A Multi-objective Network Design Model for Post-disaster Transportation Network Management

Despite their inherent vulnerability to structural and functional degradation, transportation networks play a vital role in the aftermath of disasters by ensuring physical access to the affected communities and providing services according to the generated needs. In this setting of operational conditions and service needs which deviate from normal, a restructuring of network functions is deemed to be beneficial for overall network serviceability. In such context, this paper explores the planning of post-disaster operations on a network following a hazardous event on one of the network’s nodes. Lane reversal, demand regulation and path activation are applied to provide an optimally reconfigured network with reallocated demand, so that the network performance is maximized. The problem is formulated as a bi-level optimization model; the upper level determines the optimal network management strategy implementation scheme while the lower level assigns traffic on the network. Three performance indices are used for that purpose: the total network travel time (TNTT), the total network flow (TNF) and the special origin-destination pair (OD pair) accessibility. A genetic algorithm coupled with a traffic assignment process is used as a solution methodology. Application of the model on a real urban network proves the computational efficiency of the algorithm; the model systematically produces robust results of enhanced network performance, indicating its value as an operation planning tool

An exact approach for the multi-depot electric bus scheduling problem with time windows

This study extends the multi-depot vehicle scheduling problem with time windows (MDVSPTW) to the case of electric vehicles which can recharge at charging stations located at any point of the service operation area. We propose a mixed-integer nonlinear model for the electric bus multi-depot vehicle scheduling problem with time windows (EB-MDVSPTW). Our formulation considers not only the operational cost of vehicles, but also the waiting times. In addition, it explicitly considers the capacity of charging stations and prohibits the simultaneous charging of different vehicles at the same charger. Chargers are modeled as task nodes of an extended network and can be placed at any location utilizing the charging infrastructure of a city instead of using only bus-dedicated chargers. Further, we linearize the MINLP formulation of the EB-MDVSPTW by reformulating it to a mixed-integer linear program (MILP) that can be solved to global optimality. Because EB-MDVSPTW is NP-Hard, we also introduce valid inequalities to tighten the search space of the MILP and we investigate the trade-off between the compactness and the tightness of the problem in benchmark instances with up to 30 trips. In the numerical experiments, we show that the valid inequalities reduce the problem’s compactness by increasing up to three times the number of constraints, but, at the same time, improve tightness resulting in computational time improvements of up to 73% in 20-trip instances. The implementation of our exact approach is demonstrated in a toy network and in the benchmark instances of Carpaneto et al. (1989).ISSN:0377-2217ISSN:1872-686

Identifying spatio-temporal patterns of bus bunching in urban networks

The objective of this paper is to identify hot spots of bus bunching events at the network level, both in time and space, using Automatic Vehicle Location (AVL) data from the Athens (Greece) Public Transportation System. A two-step spatio-temporal clustering analysis is employed for identifying localized hot spots in space and time and for refining detected hot spots, based on the nature of bus bunching events. First, the Spatio-Temporal Density Based Scanning Algorithm with Noise (ST-DBSCAN) is applied to distinguish bunching patterns at the network level and subsequently a k++means algorithm is employed to distinguish different types of bunching clusters. Results offer insights on specific time periods and route segments, where bus bunching events are more likely to occur and, also, on how bus bunching clusters change over time. Further, headway deviation analysis reveals the differences in the characteristics of the various bunching event types per line, showing that routes running on shared corridors experience more issues while underlying causes may vary per line. Collectively, results can help guide practice toward more flexible solutions and control strategies. Indeed, depending on the type of spatio-temporal patterns detected, appropriate improvements in service planning and real-time control strategies may be identified in order to mitigate their negative effects and improve quality of service. In light of emerging electric public transport systems, the proposed framework can be also used to determine preventive strategies and improve reliability in affected stops prior to the deployment of charging infrastructure

Developing public transport network systems: The DIANA approach

In this paper we introduce the project DIANA, which deals with the development of innovative algorithms and decision support systems for the design of public transport network systems. The project aims to design transportation networks with conventional and electric vehicle types, with the objectives of maximizing total welfare, including the minimization of system emissions. Evolutional algorithms and reinforcement learning methods are being developed for solving the associated transit route network design problem. Finally, a web-based decision support system is under development utilizing state of the art GIS technology

Developing public transport network systems: The DIANA approach

In this paper we introduce the project DIANA, which deals with the development of innovative algorithms and decision support systems for the design of public transport network systems. The project aims to design transportation networks with conventional and electric vehicle types, with the objectives of maximizing total welfare, including the minimization of system emissions. Evolutional algorithms and reinforcement learning methods are being developed for solving the associated transit route network design problem. Finally, a web-based decision support system is under development utilizing state of the art GIS technology

Towards transfer synchronization of regularity-based bus operations with sequential hill-climbing

In this work we model and discuss how we can achieve coordination between different bus service lines. Key problem challenges are (a) the multiple conflicting priorities (on one hand the improvement of bus service regularity and on the other hand the reduction of passenger transfer waiting times) and (b) the computational complexity for re-scheduling the dispatching times of bus trips for meeting the conflicting priorities. Initially, a model for reducing the waiting times at bus transfer stations while also improving the operations of regularity-based bus services subject to operational constraints is introduced. Conflicting priorities are handled with the introduction of weight factors that allow bus operators to decide the trade-off between improvement of regularity-based operations and reduction of passenger waiting times at transfer stations. After that, an exterior point penalty function is introduced for handling operational constraints and a sequential hill-climbing search strategy is applied for converging to an approximate optimal solution. For our case study, we utilize general transit feed specification data from two regularity-based bus services in central Stockholm that intersect in five transfer stations. Experimental tests showcase a 13% potential waiting time improvement at transfer stations while sacrificing only 2.8% of service regularity and satisfying all operational constraints