A methodological approach for developing and assessing business models for flexible transport systems

Flexible transport services include a wide range of demand responsive transport systems that provide non-conventional passenger and freight transportation services. Several alternative business models varying according to the local market conditions, the socio-economic, legal, and institutional framework may be developed for the provision of Flexible Transport Systems (FTS). The objective of this paper is twofold: first to present an integrated methodological framework for developing and assessing alternative FTS business models and second to demonstrate its applicability to a case study regarding the prioritization of alternative FTS business models for the provision of flexible passenger transport services in Helsinki.

A Bayesian approach for modeling origin-destination matrices

The majority of origin destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel census studies, in contrast, covers the entire population of a specific study area of interest. In such cases where reliable historical data exist, statistical methodology may serve as a flexible alternative to traditional travel demand models by incorporating estimation of trip-generation, trip-attraction and trip-distribution in one model. In this research, a statistical Bayesian approach on OD matrix estimation is presented, where modeling of OD flows derived from census data, is related only to a set of general explanatory variables. A Poisson and a negative binomial model are formulated in detail, while emphasis is placed on the hierarchical Poisson-gamma structure of the latter. Problems related to the absence of closed-form expressions are bypassed with the use of a Markov Chain Monte Carlo method known as the Metropolis-Hastings algorithm. The methodology is tested on a realistic application area concerning the Belgian region of Flanders on the level of municipalities. Model comparison indicates that negative binomial likelihood is a more suitable distributional assumption than Poisson likelihood, due to the great degree of overdispersion present in OD flows. Finally, several predictive goodness-of-fit tests on the negative binomial model suggest a good overall fit to the data. In general, Bayesian methodology reduces the overall uncertainty of the estimates by delivering posterior distributions for the parameters of scientific interest as well as predictive distributions for future OD flows.     

A multiobjective hierarchical model for locating public facilities on a transportation network: A goal programming approach

This paper introduces a Multiobjective Hierarchical Model (MOHLM) for locating public facilities on a transportation network. The proposed model combines the multiobjective nature of the location-allocation problem with the hierarchical character of some public service systems, such as health care delivery. The model examines both maximum and total weighted travel time, facility utilization, and total travel time from the master facility to the attached subordinate facilities. An iterative goal programing algorithm is used to solve the problem. An example related to the location of health care facilities in a rural area of Greece is used to illustrate the application of the proposed model.     

A framework for evaluating the dynamic impacts of a congestion pricing policy for a transportation socioeconomic system

This paper provides a modeling framework based on the system dynamics approach by which policy makers can understand the dynamic and complex nature of traffic congestion within a transportation socioeconomic system representation of a metropolitan area. This framework offers policy makers an assessment platform that focuses on the short- and long-term system behaviors arising from an area-wide congestion pricing policy along with other congestion mitigation policies. Since only a few cities in the world have implemented congestion pricing and several are about to do so, a framework that helps policy makers to understand the impacts of congestion pricing is currently quite relevant. Within this framework, improved bus and metro capacities contribute to the supply dynamics which in turn affect the travel demand of individuals and their choice of different transportation modes. Work travel and social networking activities are assumed to generate additional travel demand dynamics that are affected by travelers’ perception of the level of service of the different transportation modes, their perception of the congestion level, and the associated traveling costs. It is assumed that the, population, tourism and employment growth are exogenous factors that affect demand. Furthermore, this paper builds on a previously formulated approach where fuzzy logic concepts are used to represent linguistic variables assumed to describe consumer perceptions about transportation conditions.     

Dealing with the efficient allocation of scarce resources at congested airports

The existing slot allocation mechanism, based on the International Air Transport Association (IATA) system and its complementary version of the European Union (EU) regulation, produces rather poor capacity allocation outcomes for congested EU airports since it fails to properly match slots requested with slots allocated to airlines. Inefficiencies during the initial allocation are mainly due to the problem complexity in conjunction to limited decision support available to slot coordinators. On the other hand, substantial inefficiencies give rise to severe slot misuse and unreasonably low utilisation of airport resources running already into scarcity. The objective of this paper is to develop an optimisation-based model implementing the existing EU/IATA rules, operational constraints, and coordination procedures with the ultimate objective to better accommodate airlines’ preferences at coordinated airports through the minimisation of the difference between the requested and the allocated slot times to airlines. The results of the model are assessed and compared vis-à-vis the allocation outcome produced according to current slot coordination practice in three regional Greek airports. The proposed model produces very promising results and demonstrates that there is large room for improvement of the efficiency of the current allocation outcome in a range between 14% and 95%. The discussion of the model results is complemented by a sensitivity analysis highlighting the importance of declared capacity and the magnitude of its influence on slot allocation efficiency.     

A Real-Time Parking Prediction System for Smart Cities

A methodological framework for multiple steps ahead parking availability prediction is presented. Two different types of predictions are provided: the probability of a free space to continue being free in subsequent time intervals, and the short-term parking occupancy prediction in selected regions of an urban road network. The available data come from a wide network of on-street parking sensors in the “smart” city of Santander, Spain. The sensor network is segmented in four different regions, and then survival and neural network models are developed for each region separately. Findings show that the Weibull parametric models best describe the probability of a parking space to continue to be free in the forthcoming time intervals. Moreover, simple genetically optimized multilayer perceptrons accurately predict region parking occupancy rates up to 30 minutes in the future by exploiting 1-minute data. Finally, the real time, Web-based, implementation of the proposed parking prediction availability system is presented.     

A generic system dynamics based tool for airport terminal performance analysis

Decision making for airport terminal planning, design and operations is a challenging task, since it should consider significant trade-offs regarding alternative operational policies and physical terminal layout concepts. Existing models and tools for airport terminal analysis and performance assessment are too specific (i.e., models of specific airports) or general simulation platforms that require substantial airport modelling effort. In addition, they are either too detailed (i.e., microscopic) or too aggregate (i.e., macroscopic), affecting, respectively, the flexibility of the model to adapt to any airport and the level of accuracy of the results obtained. Therefore, there is a need for a generic decision support tool that will incorporate sufficient level of detail for assessing airport terminal performance. To bridge this gap, a mesoscopic model for airport terminal performance analysis has been developed, that strikes a balance between flexibility and realistic results, adopting a system dynamics approach. The proposed model has a modular architecture and interface, enabling quick and easy model building and providing the capability of being adaptable to the configuration and operational characteristics of a wide spectrum of airport terminals in a user-friendly manner. The capabilities of the proposed model have been demonstrated through the analysis of the Athens International Airport terminal.     

Introduction to an innovative crew composition approach based on safety/operational and financial requirements

This paper proposes a tool to estimate crew composition based on safety/operational and financial requirements. As there is a tendency of ship owners to implement improved technologies on board their vessels, there is no systematic way to predict their potential effect on crew size and composition (typically determined by flag state authorities on a case-to-case basis) nor on the type and complexity of on board duties new technologies might dictate. The main aim of this paper is to develop a tool to assist in determining crew composition, by taking into account both administration’s and the ship owner’s point of view. Based on data collected from ship owners, a data mining technique is implemented in order to form a generalized framework that estimates crew composition as a function of ship type, size, and degree of automation. The agreement of model predictions with records from specific (vessel) cases is very good in terms of safety (for operations such as watchkeeping, mooring/unmooring, loading/unloading). The specific intended use of this tool is to help a ship owner decide whether it is cost-beneficial to retrofit a conventional vessel with advanced technologies that would potentially entail a reduced crew (probably dealing with different and more complex on board duties). Its main benefits are that it can be used to estimate crew composition before any vessel construction or upgrade has actually taken place and that it allows crew composition to be easily adapted to the technological evolution of ship systems even at their current rapid pace.     

A decision support system for total airport operations management and planning

The airport planning and decision making process exhibits various trade‐offs and complications due to the large number of stakeholders having different, and sometimes conflicting, objectives regarding the assessment of airport performance. As a result, the airport performance assessment necessitates the use of advanced modelling capabilities and decision support systems or tools in order to capture the multifaceted aspects, interests and measures of airport performance like capacity, delays, safety, security, noise and cost‐effectiveness. Presently, airport decision makers lack decision support tools able to provide an integrated view of total airport (both airside and landside) operations and analyse at a reasonable effort and decision‐oriented manner the various trade‐offs involved among different airport performance measures. The objective of this paper is twofold: (i) to describe the decision‐oriented modelling framework and development process of a decision support system for total airport operations management and planning, and (ii) to demonstrate the decision support capabilities and basic modelling functionalities of the proposed system. Copyright © 2010 John Wiley & Sons, Ltd.     

Auction-Based Congestion Pricing

Abstract

Planners, engineers and economists have introduced various demand management methods in an attempt to reduce the fast growing traffic congestion. The basic idea behind various demand management strategies is to force drivers to travel and use transportation facilities more during off-peak hours and less during peak hours, as well as to increase the usage of underutilized routes. In this paper, a new demand management concept – Auction-based Congestion Pricing – is proposed and modeled.