Konstantinos Gkiotsalitis

A Mobile Application for Real-Time Multimodal Routing Under a Set of Users’ Preferences

In this article an application for mobile navigation is introduced with the intention to serve users who either do not have access to a private vehicle or prefer combining a vehicle with a public transport mode provided that it yields a reduction to their total travel time. This application guides users to their destination by integrating the use of private and public transport and differs from in-vehicle route guidance systems that suggest routes only for private vehicle users, and from other conventional mobile route guidance systems that propose either a route for public transport users or a route for private vehicle users. In addition, the proposed application suggests intermodal routes that comply with each individuals preferences in an attempt to fulfill the users needs in complex, urban networks. The data requirements for the mobile application are covered by a real-time database that is handled by a central server (processor) and contains information about traffic congestion, train and bus schedules, and more. Finally, two algorithms are presented in order to serve the computational part of the application and determine optimal paths in intermodal networks with respect to a predetermined set of users’ preferences. The data requirements, the structure and the architecture of the application, the required technologies, the underpinning mathematical context of the proposed algorithms, and the performance of this application in random networks are also presented.

Improving Bus Service Reliability with Stochastic Optimization

Bus route performance, typically expressed in terms of excess waiting time (EWT), is often unstable and suffers from bunching which results in lower regularity. Governments are introducing regularity-based contracts for operators, where monetary incentives or penalties are introduced depending on performance. Optimization of bus regularity requires the recursive coordination of several buses, hindering the solution scalability in real-time. In this work we propose a bus headway balancer based on stochastic search and branch hopping/merging algorithm which optimizes schedules to minimize the EWT. This algorithm balances bus headway deviations by introducing dwell intervals in the schedule, which can be applied off line or in real-time. A test-case implementation of the approach used 3-month AVL data from a bus operator in Asia and showcased an improvement of EWT by up to 50% with reduction in computational complexity to almost linear time and at least 2x times increase at solution space search.

Optimizing Leisure Travel: Is BigData Ready to Improve the Joint Leisure Activities Efficiency?

Over the past years we are witnessing an upsurge on the volume of travelers’ generated data. The upsurge of user-generated data from Smart Cards, Smart phones, personal navigators and social media has drawn the attention of the scientific community and new methods for utilizing such data in the areas of citizen-sensing, mobility understanding and travelers’ behavioral analysis have been developed and tested. Stepping ahead from the central problem of leveraging user-generated data for improving the scheduling of transport services, this survey paper tries to investigate the importance of big-data on improving the organizational efficiency of physical meetings among multiple travelers in urban environments. First, this work examines the state-of-the-art on capturing travelers’ patterns based on their data traces and the expected gains from leveraging user-generated data for optimizing leisure travel. Then, the problem of optimizing joint leisure travel is formulated and presented in an algorithmic form concluding to the suggestion of new research directions for future work.

Opportunistic Solution-Space Reduction Techniques for Reducing the Time Complexity of Dynamic Speed Control with Microsimulation on Motorways

Dynamic Speed Control (DSC) on motorways can be enhanced by using models with higher granularity for capturing the evolution of traffic flow. Nonetheless, the required number of micro-simulation runs for reaching an optimal DSC solution and their computational complexity hinder the use of micro-level models for DSC on large-scale motorways. The present study introduces a set of techniques that reduce the number of required micro-simulations, thus improving the computational cost. The proposed techniques: 1) split the motorway into stretches; 2) introduce an approach based on genetic algorithms to reduce the number of micro-simulations; 3) exclude, when possible, VMS combinations by applying solution approximation methods instead of micro-simulation. The method is evaluated through a number of test scenarios in a stretch of motorway A6 in the Netherlands.

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.

Bus Operations Scheduling Subject to Resource Constraints Using Evolutionary Optimization

In public transport operations, vehicles tend to bunch together due to the instability of passenger demand and traffic conditions. Fluctuation of the expected waiting times of passengers at bus stops due to bus bunching is perceived as service unreliability and degrades the overall quality of service. For assessing the performance of high-frequency bus services, transportation authorities monitor the daily operations via Transit Management Systems (TMS) that collect vehicle positioning information in near real-time. This work explores the potential of using Automated Vehicle Location (AVL) data from the running vehicles for generating bus schedules that improve the service reliability and conform to various regulatory constraints. The computer-aided generation of optimal bus schedules is a tedious task due to the nonlinear and multi-variable nature of the bus scheduling problem. For this reason, this work develops a two-level approach where (i) the regulatory constraints are satisfied and (ii) the waiting times of passengers are optimized with the introduction of an evolutionary algorithm. This work also discusses the experimental results from the implementation of such an approach in a bi-directional bus line operated by a major bus operator in northern Europe.