The Importance Of Open Data Accessibility For Multimodal Travel Improvement*

The dynamic urban network continues to face a number of problems caused by traffic. One of the main problems is the increasing use of personal vehicles (especially for shorter journeys) and an unattractive alternative – public transport. In this context, Intelligent Transport Systems can be defined as a holistic, management and information communication upgrade of the classic transport and traffic system. From the passengers’ point of view, the usage of personal vehicles is still more pronounced compared to public transport. The main reason is that the public transport service quality needs to be improved if compared to the personal vehicles. The concept of multimodal travel is not new, but with the usage of adequate Intelligent Transport Systems services, it is possible to support and encourage modal shift, optimise the use of public space and influence passengers’ behaviour patterns. Multimodal Journey Planners provide travellers with better and more complete information when choosing a mode of transport so they can select the most suitable option for their needs. The open data approach is crucial for defining a system that responds to the end-users’ actual needs and aspirations (personalisation of the service). Another major challenge in providing a high-quality multimodal journey planning service is the availability and accessibility of data. EU directives require each Member State to establish a National Access Point. The National Access Point is a digital interface, a single/unique access point providing all information regarding travel and traffic. In this article, the importance of traffic data collection, acquisition and distribution according to the open data concept is described

National freight transport planning: towards a Strategic Planning Extranet Decision Support System (SPEDSS)

This thesis provides a `proof-of-concept' prototype and a design architecture for a Object Oriented (00) database towards the development of a Decision Support System (DSS) for the national freight transport planning problem. Both governments and industry require a Strategic Planning Extranet Decision Support System (SPEDSS) for their effective management of the national Freight Transport Networks (FTN). This thesis addresses the three key problems for the development of a SPEDSS to facilitate national strategic freight planning: 1) scope and scale of data available and required; 2) scope and scale of existing models; and 3) construction of the software. The research approach taken embodies systems thinking and includes the use of: Object Oriented Analysis and Design (OOA/D) for problem encapsulation and database design; artificial neural network (and proposed rule extraction) for knowledge acquisition of the United States FTN data set; and an iterative Object Oriented (00) software design for the development of a `proof-of-concept' prototype. The research findings demonstrate that an 00 approach along with the use of 00 methodologies and technologies coupled with artificial neural networks (ANNs) offers a robust and flexible methodology for the analysis of the FTN problem domain and the design architecture of an Extranet based SPEDSS. The objectives of this research were to: 1) identify and analyse current problems and proposed solutions facing industry and governments in strategic transportation planning; 2) determine the functional requirements of an FTN SPEDSS; 3) perform a feasibility analysis for building a FTN SPEDSS `proof-of-concept' prototype and (00) database design; 4) develop a methodology for a national `internet-enabled' SPEDSS model and database; 5) construct a `proof-of-concept' prototype for a SPEDSS encapsulating identified user requirements; 6) develop a methodology to resolve the issue of the scale of data and data knowledge acquisition which would act as the `intelligence' within a SPDSS; 7) implement the data methodology using Artificial Neural Networks (ANNs) towards the validation of it; and 8) make recommendations for national freight transportation strategic planning and further research required to fulfil the needs of governments and industry. This thesis includes: an 00 database design for encapsulation of the FTN; an `internet-enabled' Dynamic Modelling Methodology (DMM) for the virtual modelling of the FTNs; a Unified Modelling Language (UML) `proof-of-concept' prototype; and conclusions and recommendations for further collaborative research are identified

Evolutionary algorithms for scheduling operations

While business process automation is proliferating through industries and processes, operations such as job and crew scheduling are still performed manually in the majority of workplaces. The linear programming techniques are not capable of automated production of a job or crew schedule within a reasonable computation time due to the massive sizes of real-life scheduling problems. For this reason, AI solutions are becoming increasingly popular, specifically Evolutionary Algorithms (EAs). However, there are three key limitations of previous studies researching application of EAs for the solution of the scheduling problems. First of all, there is no justification for the selection of a particular genetic operator and conclusion about their effectiveness. Secondly, the practical efficiency of such algorithms is unknown due to the lack of comparison with manually produced schedules. Finally, the implications of real-life implementation of the algorithm are rarely considered. This research aims at addressing all three limitations. Collaborations with DBSchenker,the rail freight carrier, and Garnett-Dickinson, the printing company,have been established. Multi-disciplinary research methods including document analysis, focus group evaluations, and interviews with managers from different levels have been carried out. A standard EA has been enhanced with developed within research intelligent operators to efficiently solve the problems. Assessment of the developed algorithm in the context of real life crew scheduling problem showed that the automated schedule outperformed the manual one by 3.7% in terms of its operating efficiency. In addition, the automatically produced schedule required less staff to complete all the jobs and might provide an additional revenue opportunity of £500 000. The research has also revealed a positive attitude expressed by the operational and IT managers towards the developed system. Investment analysis demonstrated a 41% return rate on investment in the automated scheduling system, while the strategic analysis suggests that this system can enable attainment of strategic priorities. The end users of the system, on the other hand, expressed some degree of scepticism and would prefer manual methods

Sustainable Freight Transport

This Special Issue of Sustainability reports on recent research aiming to make the freight transport sector more sustainable. The sector faces significant challenges in different domains of sustainability, including the reduction of greenhouse gas emissions and the management of health and safety impacts. In particular, the intention to decarbonise the sector’s activities has led to a strong increase in research efforts—this is also the main focus of the Special Issue. Sustainable freight transport operations represent a significant challenge with multiple technical, operational, and political aspects. The design, testing, and implementation of interventions require multi-disciplinary, multi-country research. Promising interventions are not limited to introducing new transport technologies, but also include changes in framework conditions for transport, in terms of production and logistics processes. Due to the uncertainty of impacts, the number of stakeholders, and the difficulty of optimizing across actors, understanding the impacts of these measures is not a trivial problem. Therefore, research is not only needed on the design and evaluation of individual interventions, but also on the approach of their joint deployment through a concerted public/private programme. This Special Issue addresses both dimensions, in two distinct groups of papers—the programming of interventions and the individual sustainability measures themselves