Human beings today have to face a series of problems brought by transport development — severe urban congestion, increasing number of injuries and fatalities as well as global warming caused by excessive emissions. Intelligent Transport Systems (ITS), as effective tools to solve these problems, thus have drawn much attention. In the future, it is expected that more and more ITS would be developed and applied in real practice. Before adopting ITS measures, it is necessary for policy makers to know the impacts of the ITS measure on on a large scale (e.g. national/European level). In many cases, the impacts of ITS are evaluated on a much smaller scale, for example from a microscopic traf- fic simulation or a field experiment. These effects need to be scaled up to the larger scale. There are two known scaling-up methods. The modelling method can accurately represent the large scale scenario, but requires considerable effort and a large amount of data which may not be available. Furthermore, it requires a macroscopic model of the ITS, which may be a challenge to derive. The statistical method describes the local scenarios via situational variables (like road types, vehicle types and traffic situations), classifies the local scenar- ios into categories and calculates the impacts on large scale as the weighted average of the local impacts. This method is easier and faster than the modelling method. However, the statistical method is only applicable for cases which only consider categorical situational variables, because the classification of the local scenarios into categories is not feasible when numerical situational variables are used. As a result, the statistical method is only suitable for ITS whose impacts are on the microscopic mechanisms (e.g speed and headway) and thus mainly affected by categorical situational variables (e.g road type and vehicle type). A scaling-up method to assess the impacts of ITS on traffic efficiency which is generally suitable for all ITS is still missing. To start filling this gap, this study develops a new scaling-up method for ITS that have direct network-wide influences to assess their large-scale impacts on traffic efficiency. The framework of the new scaling-up method is shown in Figure 2.2 and the graphical and mathematical interpretations are presented in Figure 2.3 and Figure 2.4. In brief, the new scaling-up method firstly chooses the suitable indicator of the impacts and situational vari- ables, then collects needed data and builds deterministic relationships between the indi- cator and the numerical situational variables, at last uses scaling sideways to calculate all local impacts and aggregates the local impacts to large scale. From the theoretical perspec- tive, the designed method is considered to be able to evaluate the impacts of ITS measures with direct network-wide influence on traffic efficiency in a large-scale scenario. To provide an evidence of the quality of the new scaling-up method, this study applies it to a specific ITS measure, that is the on-trip dynamic navigation system. Although the final large-scale impacts of the on-trip dynamic navigation system is not calculated due to the limitation of data source, it is proved that the new method is able to accurately assessthe large-scale impacts of the on-trip dynamic navigation system with enough available data. Other findings from the case study are also valuable. For example, the choice of the indicator and the situational variables, and the built deterministic relationship can be directly adopted in other projects that study the impacts of the on-trip dynamic navigation system, which indicates the practical contribution of this study. From a methodological perspective, the new scaling-up method is a great improvement of the current scaling-up approaches. The new scaling-up method expands the applica- tion area of scaling-up methods to ITS that have network-wide influences. Compared to the current methods, the new scaling-up method also improves the accuracy of scaling up and leads to more reliable assessments. Apart from the merits, there are also some dis- advantages of the new scaling-up method, such as the possibility of more time cost and data needed, as well as the possible difficulty to explain the deterministic relationships in a sensible way. The new scaling-up method is regarded to be with significant political value. The out- puts can provide useful information to support policy making. On one hand, according to the political economy model designed by Beuthe, the impacts of ITS play an important role in making policy decisions. The impacts of ITS can directly reflect the perceived effectiveness and the perceived distribution of benefits and costs. On the other hand, based on the outputs of the new scaling-up method, there are also other policy advices that could be made. For instance, the built deterministic relationship(s) can suggest the to-be-set value of the related parameters when adopting a certain ITS measure. For future researches, the attention could be focusing on applying the new method to more ITS measures and investigating the applicability of the new method on assessing the impacts on safety and environment. Specifically regarding the study of the on-trip dynamic navigation system, if the needed data is available, it would be beneficial to conduct a com- plete assessment of the large-scale impacts in a specific scenario in the future. In addition, the influences of other situational variables besides the considered situational variables could also be taken into account. Furthermore, a more specific classification of network structure is expected in future researches.