Comparison with State of the Art


5 System Impact Assessment Study: Indicative Results

6.1 Comparison with State of the Art

Before we present the anticipated deployment of the system, it is beneficial to summarise the relation of the prototype integrated system (presented in Chapter 4) to the overall market context that is being placed within.

Starting from the needs and system requirements phase, and in order to define the most representative Use Cases for the intended system together with the associated parameters/restrictions, a series of accident data sources which deal with the DG transportation in national, European and International level (i.e. FARS, MHIDAS, BP, GUNDI databases, INFORMED LdV project reported studies, etc.) has been taken into consideration. It has managed to re-classify the available data in a way that enabled the identification of those conditional parameters that are considered significant for the envisaged system implementation and evaluation, creating in this way a knowledge database around accidents and relevant information for the accidents and the status of traffic safety in the DG haulage sector. In addition, through the surveys and the workshops that were realised, it was made feasible to go one step further and detect the specific needs of all actors involved in the DG transportation chain, with regard to systems like the one being developed. The Use Cases identified is the most significant outcome of this work and can be used as a reference for other systems and for further research.

On the other hand, existing classification systems that support the current transportation schemes all around Europe, most of them ADR-based, but applied in a very specific way (according to local infrastructures and regulations requirements), have been investigated. The ontology developed (Bekiaris and Gemou, 2006) is considered to be the first most innovative outcome of the project, since there is no relevant ontology known, which addresses the special conditions existing in DG transportation and has classified a series of info about the driver, the cargo, the company, the environmental conditions, the vehicle and the logistic chain, so that the needs of all interested actors (i.e. infrastructure, drivers, customers, companies, etc.) are being addressed. Its main benefits lie in the fact that the ontology is open, easily interfaced and also amenable to further enrichment and editing.

Hellenic Institute of Transport / Centre for Research & Technology Hellas 102 Furthermore, the Route Guidance System and its embedded DSS is the first and only (so far) system that optimises routing by taking into account the risk associated with the road transport of dangerous goods in addition to the usual economic factors, such as time, distance and/or fuel consumed. Systems relevant to this can be separated into two broad categories, with no significant overlap between them. The first category encompasses systems dealing with Quantitative Risk Assessment whereas as the second deals with Vehicle Route Guidance and Optimization.

Quantitative Risk Assessment tools are based on a set of procedures, aimed at the quantitative assessment of the risks connected with processing, storage and transportation of dangerous substances in industrial areas. The risk quantification procedure is developed through the evaluation, for all risk sources, of the accidents occurrence frequency and of the magnitude of casualties caused by such events. Such tools include integrated modules for visualization, geospatial analysis, statistical analysis, human health risk assessment, ecological risk assessment, cost/benefit analysis, sampling design, and decision analysis. The main objective of all Quantitative Risk Assessment Systems is the location planning of industrial installations and/or of static transportation routes, by taking into account economic and societal factors (e.g. safety).

On the other hand, Vehicle Route Guidance and Optimization is a quickly expanding field that uses the latest advances in telematics and computing to combine real time location information with detailed knowledge of terrains, in order to provide detailed routing instructions to vehicles on the road. All commercial fleet management systems offer a core of common features, such as:

 Multiple commodity, multiple vehicle routing optimization, that takes into account delivery time windows. Parameters optimized are of a financial nature, such as fuel cost or time, distance, etc.

 Integration with enterprise logistics systems.

 Wireless, real time (satellite and mobile carrier based) monitoring, and control of vehicles to various extents.

 Real time re-routing capabilities in the case of unforeseen events or changes in business requirements.

 Logging of vehicle status.

 Advanced systems, that offer a “hazmat” routing option, that takes into account relevant accessibility regulations and restrictions for the different classes of transported goods when doing routing optimization.

Hellenic Institute of Transport / Centre for Research & Technology Hellas 103 From the analysis of available offerings in both sectors, it has been made clear that existing Quantitative Risk Assessment tools do not include facilities for dynamic vehicle routing, whereas Route Guidance systems do not take into account transport risk related factors.

The DSS is a system that integrates and builds on the most recent research efforts, combining methodologies from the area of Quantitative Risk Assessment and Vehicle Routing Optimization under real time conditions and local information. Even though other existing systems overlap with particular areas covered by this system and most of the relevant technologies and know-how deployed are available, the integrated functionality offered by this system DSS is truly unique and novel. The following table presents a comparison of the characteristics of our system DSS in relation to other existing systems.

Table 6.1: Comparison of the characteristics of the system DSS in relation to Quantitative Risk Analysis Tools and Route Guidance Systems

Characteristic GoodRoute

DSS Risk Analysis Quantitative Tools

Route Guidance


GIS back-end   

Quantitative Risk Assessment  

Evaluation of Risk measures (Individual &

societal risks, F/N curves etc.)  

Vehicle Routing Optimization  

Minimum Risk Routing 

Use of real time traffic data  

Use of local road characteristics  

Use of local weather statistical  

Use of real time weather data 

Consideration of broader needs of society  

Even though there are significant challenges to be met before the system can be implemented to its fullest extent, significant gains can be expected from even an incomplete initial realisation, thereby making the existence of a critical market mass for profitable implementation unnecessary. Challenges to overcome relate mostly to the lack of detailed data from which accurate Quantitative Risk Assessment calculations can be made. However, even when using data with low time and space granularity, useful decisions can be made with regard to the routing of DGVs. Another significant challenge, that is being currently overcome, is the necessity of performing the numerically very intensive calculations, related to the calculation of transport risks over an entire road network in real or almost real time.

Hellenic Institute of Transport / Centre for Research & Technology Hellas 104 Considering the given total lack of commercial systems similar to this one, in combination with the vigorous research interest apparent in the recent literature and the obvious benefits to society, it becomes clear that there should be significant commercialization opportunities for the developed system DSS and Route Guidance system. However, much concerted work remains to be done in terms of building the necessary information infrastructure, by the private as well as by the public sector, for sustainable real world implementation. The effort is certainly worthwhile, given the opportunities involved.

Another important module developed and is part of its integrated configuration is the one dedicated to enforcement. Studies performed in the EU Member States showed that good enforcement practices could avoid many road fatalities resulting from speeding, not wearing seat belts or driving while intoxicated; moreover non compliance with rules relating to professional road transport activities, such as driving and resting times or weight and dimensions, for trucks and buses, is an important cause of fatal accidents. According to the Directive 2006/22/EC on social legislation relating to “Road transport activities”, the introduction of digital tachographs (DTCO) has become mandatory in all EU Member States commercial vehicles. The Directive defines checks to be undertaken, resting times and the proper operation of the tachograph and associated equipment. Some constraints have been introduced on storage duty of diagram charts in the vehicle, manual recordings and printout and their safekeeping period has been prolonged. Drivers, on request, must be able to present diagram charts, manual recordings and printouts for the current week plus the preceding 15 days.

Our case study refers in particular to vehicles transporting goods that, in case of accident, are dangerous for the environment and people’s health. The enforcement module developed is connected to DTCO and anticipates the capability to transmit through wireless communication links driver and vehicle information to control centres and infrastructure nodes.