How Do Governments Support the Development of Public Private Partnerships? Measuring and Comparing PPP Governmental Support in 20 European Countries

Abstract

Taking an institutional perspective, in this article we develop an index of the governmental support for public private partnership (PPP) — a ‘PPP Governmental Support Index’ (GSI) — which aims to measure the extent to which national governments provide an institutional framework that is either conducive or preventive for the introduction and diffusion of PPPs within transport infrastructure and other sectors. First, based on a substantive review of the literature, we define the elements of the PPP GSI, including the policy and political commitment regarding PPPs, the legal and regulatory framework, and the presence/absence of dedicated PPP-supporting arrangements. Second, we calculate the PPP GSI for 20 European countries, cluster them and compare similarities and differences in national governmental support of infrastructure PPPs. Third, we explore the potential link between national institutional index scores and infrastructure PPP activity in the 20 countries. Lastly, we discuss the potential and usefulness of the presented PPP GSI, as well as methodological limitations, and elaborate on how this index might be utilised to strengthen future comparative research on PPP in transport and other sectors.     

Investigating the predictive performance of computational process activity-based transportation models

The aim of this paper is to achieve a better understanding of computational process activity-based models, by identifying factors that influence the predictive performance of A Learning-based Transportation Oriented Simulation System model. Therefore, the work activity process model, which includes six decision steps, is investigated. The manner of execution in the process model contains two features, activation dependency and attribute interdependence. Activation dependency branches the execution of the simulation while attribute interdependence involves the inclusion of the decision outcome of a decision step as an attribute to subsequent decision steps. The model is experimented with by running the simulation in four settings. The performance of the models is assessed at three validation levels: the classifier or decision step level, the activity pattern sequence level and the origin–destination matrix level. The results of the validation analysis reveal more understanding of the model. Benchmarks and factors affecting the predictive performance of computational activity-based models are identified.     

A validation measure for computational scheduler activity-based transportation models based on sequence alignment methods

In recent decades, activity-based transportation models have gained growing attention, due to their strong foundation in behavioral theory and ability to model the response of individuals to travel demand management policies. Hence, researchers have become increasingly interested in analyzing and predicting individuals’ decisions about activity participation. This paper investigates the reliability and uncertainty of computational process activity-based models. The design of the scheduling process model is experimented with by introducing an alternative decision sequence. The results provide additional information to better understand the process model’s reliability and behavior. Furthermore, the findings show that the current sequence of decision steps in the process model in ALBATROSS achieves satisfactory work activity schedules. Finally, the study concludes that using a decision tree model achieves a better performance than using diverse data mining approaches.     

Measuring dissimilarity of geographically dispersed space�Ctime paths

In activity-travel analysis, sequences are analysed both in space and time. From this perspective, sequence alignment methods (SAM) are used to value the dissimilarity of sequences. However, only a limited number of research efforts account for spatial characteristics of activity-travel sequences. Additionally, the existing techniques considering spatial characteristics are mainly suited to compare sequences within a small study area. Therefore, the present research re-designs a multidimensional dissimilarity measure which enables identifying dissimilarities between sequences which are geographically dispersed. This technique includes transforming the geographical coordinates of activity locations to Angle/Arc Length (AAL)-trajectories to capture the relative geographical movements within each sequence. These AAL-trajectories form the basis of the subsequent multidimensional sequence alignment analysis aimed at estimating the dissimilarity between activity-travel sequences. This approach proves to compare activity-travel sequences based on the relative positions of the activity locations within sequences, rather than founded on the distances between the absolute geographical locations, as is the case in the traditional sequence alignment methods.     

The usefulness of the Sequence Alignment Methods in validating rule-based activity-based forecasting models

This research paper aims at achieving a better understanding of rule-based activity-based models, by proposing a new level of validation at the process model level in the A Learning-based Transportation Oriented Simulation System (ALBATROSS) model. To that effect, the work activity process model, which includes six decision steps, has been investigated. Each decision step is evaluated during the prediction of the individuals?? schedules. There are specific decision steps that affect the execution pattern of the work activity process model. So, the comportment of execution in the process model contains activation dependency. This branches the execution and evaluation of each agent under examination. Sequence Alignment Methods (SAM) can be used to evaluate how similar/dissimilar the predicted and observed decision sequences are on an agent level. The original Chi-squared Automatic Interaction Detector decision trees at each decision step utilized in ALBATROSS are compared with other well known induction methods chosen to appraise the purpose of the analyses. The models are validated at four levels: the classifier or decision step level whereby confusion matrix statistics are used; The work activity trips Origin?CDestination matrix level; the time of day work activity start time level, using a correlation coefficient; and the process model level, using SAM. The results of validation on the proposed process model level show conformity to all validation levels. In addition, the results provide additional information in better understanding the process model??s behavior. Hence, introducing a new level of validation incur new knowledge and assess the predictive performance of rule-based activity-based models. And assist in identifying critical decision steps in the work activity process model.     

On the experimental analysis of single input single output control of yaw rate and sideslip angle

Electric vehicles with individually controlled drivetrains allow torque-vectoring, which improves vehicle safety and drivability. This paper investigates a new approach to the concurrent control of yaw rate and sideslip angle. The proposed controller is a simple single input single output (SISO) yaw rate controller, in which the reference yaw rate depends on the vehicle handling requirements, and the actual sideslip angle. The sideslip contribution enhances safety, as it provides a corrective action in critical situations, e.g., in case of oversteer during extreme cornering on a low friction surface. The proposed controller is experimentally assessed on an electric vehicle demonstrator along two maneuvers on surfaces with significantly varying tire-road friction coefficient. Different longitudinal locations of the sideslip angle used as control variable are compared during the experiments. Results show that: i) the proposed SISO approach provides significant improvements with respect to the vehicle without torque-vectoring, and the controlled vehicle with a reference yaw rate solely based on the handling requirements for high-friction maneuvering; and ii) the control of the rear axle sideslip angle provides better performance than the control of the sideslip angle at the center of gravity.