A method for assessing potential synergies among different sets of transport telematic services (TTSs) is suggested. An Intelligent Transport System enhances transport by delivering one or more TTSs. The ability to deliver multiple TTSs to address a wide range of stakeholder needs is gaining momentum, not only from a marketing perspective but also from a technological perspective. The total cost of TTSs can be reduced if they share functionalities (i.e., sub-services provided by telematic systems). We show how this synergy can be assessed with the help of clustering methods. Knowledge about possible synergies of functionalities is useful in the (re)design and eventual deployment of TTSs, especially when the underlying telematic systems are able to support multiple TTSs. To adapt the clustering method for this purpose, we suggest a mathematical formulation of synergy among functionalities of TTSs. By applying the method to a set of 32 TTSs, we obtain a cluster formation of these TTSs according to their synergy measures. Overall, the results suggest that the joint implementation of TTSs targeted toward some problem domains can lead to significant cost savings, for example, Road User Charging, Infrastructure Repair and Maintenance, and Information on the Transport of extra large goods for the management of road transport infrastructure. © The Institution of Engineering and Technology 2015.

The efficiency of road transport is typically influenced by factors such as, weather, choice of road, and time of day, and day of the week. Knowledge about interactions between different traffic-and transport related factors and their influence on the execution of transport is important in transport planning. The purpose of this paper is to study the impact of different factors on the performance of road transport. We aim to contribute to improved transport planning by analysing traffic and transport data obtained from different sources in order to support data driven decision making. Through a review of existing literature and discussions with a Swedish road transport operator, we identified factors that could be relevant to consider when planning a transport, e.g., related to weather, location of roads where the transport will take place, and planned time of the transport. As a result of variation in size, type and volume of the data representing these factors, suitable machine learning algorithms were selected, such as Decision Stump, M5 model tree, M5 regression tree, RepTree, M5 rules, and linear regression in order to study the data. Our experimental results illustrate the complexity associated to the performance of road transport systems mainly because of the dependency between the choices of influencing factors and geographic location of the road segment.

The selection, composition and integration of Transport Telematic Services (TTSs) is crucial for achieving cooperative Intelligent Transport Systems (ITS). To enable future adaptation, models for selecting and composing TTSs needs to take into account possible future modifications, upgrades or downgrades of different TTSs without tipping off the benefit edge. To achieve this, a Strategic Service Selection Problem (SSSP) for TTSs is presented in this article. The problem involves selecting a set of TTSs that maximizes net societal benefits over a strategic time period, e.g., 10 years. The formulation of the problem offers possibilities to study design alternatives taking into account future modifications, extensions, upgrades or downgrades of different TTSs. Two decisive factors affecting the choices and modifications of TTSs are studied: 1) the effect of using Governmental policies to mandate the introduction of TTSs, e.g., Road User Charging and eCall, and 2) the effects of allowing market forces to drive the choices of TTSs. Case study results indicate that in determining combinations of TTSs that can be deployed in a period of 10 years, enforcing too many TTSs can retard the ability of the market to generate net benefits even though the results could be that more TTSs will be deployed.

The aim of this paper is to identify and analyze important factors related to the benefits of Intelligent Truck Parking (ITP) for different stakeholders (including the end users) in the context of Heavy Goods Vehicle (HGV) transport. Previous work has neither focused on different types of ITP benefits for HGVs, nor on the end user perspective. This article identifies benefit areas and attributes as well as stakeholder groups relevant for HGV transport based on a review of previous research and projects. These benefit areas and attributes are theoretically assessed and compared for different stakeholders using multi-criteria analysis. Additionally, interview results of Swedish drivers’ and companies’ perceptions of ITP are presented. Comparing results of the interviews with the theoretical assessment indicates that the end users may not perceive ITP as highly beneficial although they theoretically benefit the most. Companies, particularly national haulage companies, express a low willingness to pay for ITP. Both the theoretical assessment and interviews show the potential for ITP to deliver different benefits to different actors across the transport chain.

Intelligent Truck Parking (ITP) involves efficient management of information as a tool for addressing some of the problems related to truck parking, such as, the inability to navigate to, or reserve a parking space. Different telematic services (ITP core services) may be required to address different stakeholder needs, e.g., a navigation service for finding the way to a truck parking area, and a reservation service for assuring the availability of a parking space when in the parking area. In addition to ITP core services, there are several other telematic services that address transport problems in general. If the synergies of different services are taken into account during design and deployment of telematic services, cost reduction may be achieved through functionality sharing. In this article, we have identified services that are relevant for sharing functionalities with ITP core services, estimate functionality costs and perform a synergy analysis to assess the marginal cost savings as a result. Overall results indicates that Traffic Information Broadcasting (TIB), Estimated Time of Arrival (ETA) for goods, and Geo-fencing (GEO) can form clusters with ITP core services that could lead to better cost savings compared to the rest of the services studied.

In this thesis, methods for using computer-based models as support tools for assessing Transport Telematic Services (TTSs) are studied. Such assessments provide one way to understand how TTSs can address problems caused by transportation, such as accidents, emissions, and energy consumption. TTSs are services based on telematic systems which are Intelligent Transport Systems (ITS) involving the integrated use of information and communication technologies in transport. The focus is on TTSs that are relevant for road freight transport, even though the suggested methods can easily be adapted for TTSs in other areas. We characterize TTSs, e.g., in terms of their functionalities, and apply computer-based modeling for pre-deployment assessment of various TTSs (from an ex-ante perspective). By analyzing information provided by the suggested computer-based models, it is possible to make an informed decision whether to (or not to) deploy a given TTS. A review of previous studies reveals information about relevant TTSs for freight transport in areas such as driver support, administration, safety, traffic management, parking, and goods handling. A hierarchical clustering algorithm and a k-minimum spanning tree algorithm were employed to analyze synergies of TTSs. Synergies can enable identification of sets of TTSs that can lead to cost savings if deployed on a common platform (cf. Multi-Service Architectures). An analytical model inspired by the net present value concept is used to estimate quantified societal benefits of TTSs. An optimization model is formulated and solved using a branch and bound method to determine an optimal combination of TTSs taking into consideration societal benefits, costs, dependencies, and synergies. The optimization model also addresses possible system architectures for achieving multiple TTSs. Dominance rough set approach is used to assess and compare benefit areas for TTSs specific to truck parking. The benefit areas are suggested with the help of conceptual modeling, which describes functional models of a system in terms of states, transitions among states, and actions performed in states. The main scientific contributions of the thesis are in suggesting new quantitative models, extending and applying existing models in the assessments of TTSs, and obtaining results that can help decision-makers select TTSs for medium-to long-term investments. Researchers can employ and build on the proposed methods when addressing different scenarios (geographic or organizational) involving similar TTSs. By studying a range of TTSs and possible Multi-Service Architecture concepts for such TTSs, the thesis contributes to achieving convergence of TTSs in a Multi-Service Architecture environment that will improve cost efficiency, minimize redundancies, and encourage the establishment of standards in the deployment of TTSs in road freight transport. TTSs implemented in such an environment can contribute to optimizing available capacity, accuracy, speed, and efficiency of road freight transport systems.

The ability to generate accurate travel time predictions for road freight transport is important when, for example, estimating the arrival times for heavy goods vehicles (HGVs) in order to plan terminal activities. We present a micro-level sampling method for road travel time prediction. The method makes use of historical GPS-data in order to determine the movement of a vehicle from an origin to a destination along a specific route. The method generates a travel time distribution, which can be used to obtain the expected travel time and probabilities for deviations. The method has been illustrated and evaluated in an experiment where the effective travel time was predicted for transport between two terminals. The experiment made use of GPS data that was recorded for two HGVs during a period of two months. An important feature of the method is that it does not need road network information, such as speed limits and number of lanes.

This study describes transport telematic services (TTSs) for road-based heavy goods vehicle (HGV) transport and suggests a method for assessing the societal value of different TTSs. For decision making related to the selection of services to promote by potential investors, for example, governmental organisations and service providers, quantified service value can simplify the decision process by enabling comparison between TTSs. Moreover, these values can serve as inputs to quantitative analysis of service architectural system designs. The authors suggest a method for assessing the societal values of TTSs using potential saving indicators (PSIs), estimated in the context of Swedish HGV freight transport. To illustrate the proposed method, 32 services are analysed, and their societal values were quantified and compared for the Swedish HGV market. Results based on estimated values of PSIs and potential percentage savings indicate the following HGV-based TTSs to be of high societal potential: transport resource optimisation, dynamic traffic information, navigation, road hindrance warning, theft alarm and recovery, accident warning information, intelligent speed adaptation, eCall, en-route driver information, transport order handling, road user charging and sensitive goods monitoring.

Transport telematic systems, also known as intelligent transportation systems, can be expensive to implement but the services they provide may offer substantial benefits. However, what services the system can provide depends on the architectural choices made, which also affects the cost of the system. We propose an optimization model to support a more informed decision before investing in a multiple service transport telematic system. The model evaluates the possible choices of services and architectures, and aims to maximize the total net societal benefit. We argue that the optimization model can provide support for strategic decisions by highlighting the consequences of adopting different system architectures, including both societal value and cost. This can be useful for decision makers, such as governments, road transport telematic service providers, and commercial road freight transport operators.

This article suggest Transport Telematic Services (TTSs) to facilitate the realization of Intelligent Truck Parking (ITP) concept i.e. ITP core services e.g. a service that can support real time reservation of truck parking space using for instance mobile phone messaging capabilities. We consider ITP to include the collection, processing and delivery of information about Truck Parking Areas (TPAs) that may take place during the planning and execution of transportation services. A key feature for ITP core services is to achieve efficient information exchange related to TPA so as to improve the overall performance of the transport system. We suggest core services seen to have a potential for achieving ITP. Our suggestions are based on studying “what” kind of information exchanges among TPA stakeholders “when” and “where” the information is exchanged in relation to transport related challenges that are addressed with such information. Hence we suggest Truck Parking facility Utilization (TPU), Goods and Vehicle Safety assurance information (GVS), Parking Location Guidance (PLG) and Parking Reservation (PAR), as ITP core services.