Given the fact that the existing literature lacks the real-time estimation of road capacity and incident location using data from inductance loop detectors, a data-driven framework is proposed in this study for real-time incident detection, as well as road capacity and incident location estimation. The proposed algorithm for incident detection is developed based on the variation in traffic flow parameters acquired from inductance loop detectors. Threshold values of speed and occupancy are determined for incident detection based on the PeMS database. The detection of the incident is followed by the real-time road capacity and incident location estimation using a Cell Transmission Model (CTM) based approach. The data of several incidents were downloaded from PeMS and used for the development of the proposed framework presented in this study. Results show that the developed framework detects the incident and estimates the reduced capacity accurately. The location of the incident is estimated with an overall accuracy of 92.5%. The performance of the proposed framework can be further improved by incorporating the effect of the on-ramps, off-ramps, and high-occupancy lanes, as well as by modeling each lane separately.

Road transport plays an important role in economic development, trade and social integration. However, it is also responsible for certain negative impacts on environment and society. In order to achieve sustainable development, there is a growing need for a country to assess both the benefits from road transport and its undesirable costs, to compare them with those in other countries and to provide estimates of their future developments. In this research, starting from the identification of relevant factors (both desirable and undesirable), a benchmarking approach is proposed as an alternative way to assess sustainable road transport among the 28 EU countries. Meanwhile, the basic concept of data envelopment analysis (DEA) for benchmarking purpose is further extended to integrate both the desirable achievements and the undesirable costs in evaluation. As a result, an overall road transport sustainability score is computed for the 28 EU countries, which can be further split into two efficiency scores with respect to desirable and undesirable factors, respectively. Furthermore, after applying a clustering analysis that groups countries with inherent similarity in their practices, the model is adopted to identify best-performing and underperforming countries within the cluster, as well as the reference sets for those underperforming ones, based on which valuable insights are gained, enabling decision makers to prioritize their actions towards a more sustainable road transport system in the future.