Dutch Highways Research Articles

Safety of Perception Systems for Automated Driving: A Case Study on Apollo

The automotive industry is now known for its software-intensive and safety-critical nature. The industry is on a path to the holy grail of completely automating driving, starting from relatively simple operational areas like highways. One of the most challenging, evolving, and essential parts of automated driving is the software that enables understanding of surroundings and the vehicle’s own as well as surrounding objects’ relative position, otherwise known as the perception system. Current generation perception systems are formed by a combination of traditional software and machine learning-related software. With automated driving systems transitioning from research to production, it is imperative to assess their safety. We assess the safety of Apollo, the most popular open-source automotive software, at the design level for its use on a Dutch highway. We identified 58 safety requirements, 38 of which are found to be fulfilled at the design level. We observe that all requirements relating to traditional software are fulfilled, while most requirements specific to machine learning systems are not. This study unveils issues that need immediate attention; and directions for future research to make automated driving safe.

Optimal service station design for traffic mitigation via genetic algorithm and neural network

This paper analyzes how the presence of service stations on highways affects traffic congestion. We focus on the problem of optimally designing a service station to achieve beneficial effects in terms of total traffic congestion and peak traffic reduction. We propose a genetic algorithm based on the recently proposed Cell Transmission Model with service station (CTM-s), that efficiently describes the dynamics of a service station. Then, we leverage the algorithm to train a neural network capable of solving the same problem, avoiding to implement the CTM-s. Finally, we validate the performance of our algorithms by using real data from Dutch highways.

Renovation of Nijkerker bridge - Innovative procurement and intensive collaboration lead to optimal approach

The Nijkerker bridge (est. 1965) proved to have insufficient structural capacity to carry the current traffic loads. A contemporary way of collaboration between Rijkswaterstaat (the Dutch Highway Agency) and private parties was introduced for this project. After the design phase, renovation of the bridge was chosen instead of demolition and construction of a new bridge. Due to the collaboration between the parties involved, an optimal solution was found to increase the lifespan of the bridge by the demanded 30 years and minimize the traffic hindrance during the renovation of the bridge.

Supervisory control of roadside units

A third of the Dutch highways is monitored and controlled by roadside units. These RSUs are safety-critical systems, as they safeguard roadworks and accidents. Therefore, it is essential to have guarantees on the correctness of the roadside unit controller. Using supervisory control theory, a correct-by-construction supervisor is obtained from a model of the system and its requirements. From the supervisor model, a controller implementation can be obtained automatically.There are many roadside units sharing the same basic components, but in different numbers and configurations. To take advantage of this, parametric models are used. Subsequently, these models can be used to create different models for different configurations of an roadside unit. The purpose of this paper is to show the applicability of supervisory control theory to roadside units. To this end, parametrized models of the components of an roadside unit are presented. The models are used to synthesize a supervisor for a specific roadside unit, which is validated using simulation. After validation, a controller implementation is generated from the supervisor model. The implementation is then validated using hardware-in-the-loop simulation.

Mapping the photovoltaic potential of the roads including the effect of traffic

After developing the methodology, we applied it to the case of the Netherlands highways. We show that the average irradiation on the Dutch highway network is around 880 kWh/m2/y, 35% less than the potential of an optimally tilted conventional PV system in the south of the Netherlands. Covering the entire 1600 km of the Dutch highways network with solar road modules of poly c-Si, mono c-Si and CIGS would respectively generate 5.2 TWh/y, 6.6 TWh/y, and 3.4 TWh/y of DC electricity. This could be used to fully power the Dutch national public lighting demand. Moreover, to include the effect of traffic on these values, a model was developed to account for the energy potential reduction due to vehicles shading. Using real traffic data from two of the top-four busiest roads in the Netherlands, the A12 and A16, it was found that traffic accounts for an average of 3% reduction of solar road irradiation and DC yield potential. The maximum reduction of 9% was observed in particular locations, such as bridges and nearby ramp roads. The result of such mapping methodology could serve as a useful tool for research advisory, private industry, and governmental projects.

A compact and scalable representation of network traffic dynamics using shapes and its applications

The biggest challenge of analysing network traffic dynamics of large-scale networks is its complexity and pattern interpretability. In this work, we present a new computationally efficient method, inspired by human vision, to reduce the dimensions of a large-scale network and describe the traffic conditions with a compact, scalable and interpretable custom feature vector. This is done by extracting pockets of congestion that encompass connected 3D subnetworks as 3D shapes. We then parameterize these 3D shapes as 2D projections and construct parsimonious feature vectors from these projections. There are various applications of these feature vectors such as revealing the day-to-day regularity of the congestion patterns and building a classification model that allows us to predict travel time from any origin to any destination in the network. We demonstrate that our method achieves a 44% accuracy improvement when compared against the consensus method for travel prediction of an urban network of Amsterdam. Our method also outperforms historical average methods, especially for days with severe congestion. Furthermore, we demonstrate the scalability of the approach by applying the method on the entire Dutch highway network and show that the feature vector was able to encapsulate the network dynamics with a 93% prediction accuracy. There are many paths to further refine and improve the method. The compact form of the feature vector allows us to efficiently enrich it with more information such as context, weather and event without increasing the computational complexity.

Beware Thy Bias: Scaling Mobile Phone Data to Measure Traffic Intensities

Mobile phone data are a novel data source to generate mobility information from Call Detail Records (CDRs). Although mobile phone data can provide us with valuable insights in human mobility, they often show a biased picture of the traveling population. This research, therefore, focuses on correcting for these biases and suggests a new method to scale mobile phone data to the true traveling population. Moreover, the scaled mobile phone data will be compared to roadside measurements at 100 different locations on Dutch highways. We infer vehicle trips from the mobile phone data and compare the scaled counts with roadside measurements. The results are evaluated for October 2015. The proposed scaling method shows very promising results with near identical vehicle counts from both data sources in terms of monthly, weekly, and hourly vehicle counts. This indicates the scaling method, in combination with mobile phone data, is able to correctly measure traffic intensities on highways, and thereby able to anticipate calibrated human mobility behaviour. Nevertheless, there are still some discrepancies—for one, during weekends—calling for more research. This paper serves researchers in the field of mobile phone data by providing a proven method to scale the sample to the population, a crucial step in creating unbiased mobility information.

Impacts of large-scale truck platooning on Dutch highways

Abstract Autonomous driving has a potential to improve the future road transportation. Truck platooning is one of the main solutions. However, the impact of truck platooning on traffic flow and safety on highways is still unclear. This research focuses on impacts of large-scale truck platooning on Dutch highway. It applied a microscopic modeling to simulate the impact of truck platooning within critical traffic locations such as merging and diverging areas. The results showed that large-scale truck platooning has an impact on the traffic safety and efficiency within these traffic locations compared to the current situation.

Using huge amounts of road sensor data for official statistics

On the Dutch road network, about 60,000 road sensors are located of which 20,000 sensors are on the Dutch highways. Both vehicle counts and average speed are collected each minute and stored in the National Traffic Daffic statistics several methodological challenges needed to be solved. The first was developing a method to check and improve the data quality as quite some sensors lacked data for many minutes during the day. A cleaning and estimation step was implemented that enabled a precise and accurate estimate of the number of vehicles actually passing the sensors for each minute. The second challenge was monitoring the stream of incoming and outgoing data and controlling this fully automatic statistical process. This required defining quality indicators on the raw and processed sensor data. The fourth challenge was determining calibration weights based on the geographic locations of the road sensors on the roads. This was needed because road sensors are not uniformly distributed over the road network. As the number of active sensors fluctuates over time, the weights need to be determined periodically. As a result of these steps accurate numbers could be produced on the traffic intensity during various periods on regions in the Netherlands.

At Rijkswaterstaat, the Dutch highways authority, Kai Feldkamp is exploring the potential around connected vehicle technology even as the direction of future communications channels remains unclear

At Rijkswaterstaat, the Dutch highways authority, Kai Feldkamp is exploring the potential around connected vehicle technology even as the direction of future communications channels remains unclear

Roadway to self-healing highways with integrated wireless electric vehicle charging and sustainable energy harvesting technologies

Development of electric mobility and sustainable energy result in new technologies such as contactless electric vehicle charging and roadway energy harvesting methods, but also self-healing asphalt roads. By combining these technologies a new concept of Future Sustainable Roads for Electric Mobility is created and presented in the paper. This paper bridges the gap created by these unilateral technology developments using a multi-disciplinary approach including placing cautions when necessary and suggesting viable alternatives for optimal utilization of these energy transfer and conversion techniques. Through theoretical analysis, simulations, and tests on lab-scale experimental prototypes, the impact of our proposal is showcased. Thermal and loss models are developed for self-healing asphalt. Also, integration study of solar roads and contactless charging is performed. Applying the insight gained from the results, it is discussed how some challenges also pave a way towards interesting opportunities, for instance, infrastructure sharing for material use optimization and efficient mosaic integration. Finally, an economic viability case study is presented for a future Dutch highway with such newly emerging components.

Recent Developments and History of the Dutch HCM

Around 1995 the need for a Dutch Highway Capacity Manual (HCM) was felt within Rijkswaterstaat (Rijkswaterstaat is the executive body of the Dutch Ministry of Infrastructure and the Environment). Previously the American HCM was used, but specific circumstances in the Netherlands made it necessary to develop a Dutch HCM. This led to the introduction of the Dutch HCM, Handboek Capaciteitswaarden Infrastructuur Autosnelwegen in 1999, followed by the 2nd edition in 2002. Then, after a long period a completely new 3rd edition was published in 2011 and a fourth edition was completed in 2015. More than 15 years of research on highway capacities and the experience in using the manual has resulted in a practical handbook with an obligation to use it in planning studies for new and renewed highways in the Netherlands. The manual is one of the guidelines implemented in a working process system for planning studies. Although different indicators have been used over the years in transportation policy, the volume-to-capacity ratio is still the main indicator for the traffic flow quality in the Dutch HCM. Before the introduction of the Dutch HCM (< 1999) this indicator had been used most frequently. The most recent fourth version of Dutch HCM includes the research of capacities of tapers, cloverleaves, rush hour lanes and 4 lanes freeways. Weaving sections received special attention, as they are still simulated with the microsimulation model FOSIM. The new information was mostly added after requests of users. Because of a bigger role of consultants and less involvements of experts of Rijkswaterstaat in the highway designing process, guidelines become much more obligatory and nowadays contain specific minimum values and other specifications of the highway elements. Analysis has also been performed of capacity measurements at more than 200 locations. The influence of, among others, lane width, speed limit, absence of emergency lanes, tunnels and bridges have been analyzed. Also different road work configurations have been analyzed. The results from these analyses have been used in the fourth version of the Dutch HCM to state the influence of different infrastructure elements on the capacity and to validate and update previously stated values.

Barriers to realizing a stewardship relation between client and vendor: the Best Value approach

This study identifies barriers to realizing a stewardship relation instead of a classic principal–agent relation between client and vendor through implementation of an innovative procurement and risk management method, the Best Value approach. This approach focuses on calculus-based trust development through the use of metrics and alignment of goals and risks between client and vendor. Barriers to realizing a stewardship relation are analysed with the use of a theoretical framework that incorporates agency theory and stewardship theory. Data was collected in 2013 and 2014 through project documentation and interviews for four case studies at the Dutch Highways Agency. Collected data was used to analyse the roles of the client and vendor, the scope and level of the products and the overall process of the clarification phase of the Best Value approach to identify barriers in practice. Case study findings are analysed and explained by agency theory and stewardship theory. From a theoretical perspective the Best Value approach shows large similarities with stewardship theory. In practice, however, the cases show that the often traditional background of project teams and the lack of experience with Best Value result in a control-oriented relationship between client and vendor characterized by information asymmetry, lack of transparency and distrust. These characteristics of the relationship between client and vendor inhibit the realization of a stewardship relation using the Best Value approach.

Community engagement in project organization research: the contextualization of the research process

In recent years a growing number of academics have proposed new ways of engaging with practitioners and other individuals and groups outside the academic world. The main aim of the movement towards more engaged research is to foster and establish forms of knowledge production in which different professional communities interact and co-operate. Community-engaged research seeks to overcome the separation of the knower from what is to be known and, by doing so, to produce knowledge that advances both science and practice. The paper reports on the experiences and insights gained during the adoption of community-engaged research at the Dutch Highways and Waterways Agency. It develops the argument that any form of community-engaged research in engineering project organization research is a dialectal and reciprocal learning process of academics and practitioners embedded in the changing context of practice. Research activities and engagement phases interactively evolve and through this interaction the research process becomes contextually dependent.

Potential Energy Savings by Reducing Rolling Resistance of Dutch Road Pavements

The current study focuses on options to improve vehicle energy efficiency by reducing rolling resistance on Dutch national highways. Different studies of pavement materials have been evaluated, and models have been compared to experimental data to review rolling resistance indicators. The study shows that texture parameters MPD (mean profile depth) and RMS (root mean square) are relevant indicators for rolling resistance, whereas the effect of road roughness (IRI) is found to be larger than indicated in the evaluated models. The effects of other wavelength regions, texture orientation, and road wear need further investigation. The rolling resistance assessment shows that single-layer porous asphalt concrete (PAC) with aggregate size 6/16 (which is most currently used on Dutch highways) has rolling resistance levels that are 8-10% higher than the levels of double-layer porous asphalt concrete (DPAC) with aggregate size 2/6. Switching to DPAC 2/6 for highways would result in energy savings of 2-2.5%. These calculations are based on estimations and results with high uncertainty and should therefore be taken as a rough estimate of the potential energy savings. Further research is recommended to further refine and validate the results of this study.

Road congestion and incident duration

Non-recurrent congestion is frequently caused by accidents and other incidents. We estimate the causal effect of incident duration on drivers' time losses through changes in non-recurrent road congestion on Dutch highways. We demonstrate that incident duration has a strong positive, but concave, effect on non-recurrent congestion. The duration elasticity of non-recurrent congestion is about 0.35 implying that a one minute duration reduction generates a €57 gain per incident. We also show that at locations with high levels of recurrent congestion, non-recurrent congestion levels are considerably higher. At very congested locations, the benefit of reducing the incident duration by one minute is about €1200 per incident. Public policies that prioritize duration reductions at congested locations are therefore more beneficial.

Road Congestion and Incident Duration

Non-recurrent congestion is frequently caused by accidents and other incidents. We estimate the causal effect of incident duration on drivers’ time losses through changes in non-recurrent road congestion on Dutch highways. We demonstrate that incident duration has a strong positive, but concave, effect on non-recurrent congestion. The duration elasticity of non-recurrent congestion is about 0.40 implying that a one minute duration reduction generates a €60 gain per incident. We also show that at locations with high levels of recurrent congestion, non-recurrent congestion levels are considerably higher. At very congested locations, the benefit of reducing the incident duration by one minute is about €500 per incident. Public policies that prioritize duration reductions at congested locations are therefore more beneficial.

Experiences in development and maintenance of Silence-GIS

Silence is a GIS (Geographical Information System) build on ArgGIS 9.3.1. This large scale noise management system has been designed for the Dutch Highway Authorities. The aim of the system is to support decisions on Dutch and European noise policy and to predict the effect of future measures on the noise exposure of the population in The Netherlands. The information needed to perform noise calculations was divided over different departments. There was a great need for standardization and integration of the different data sets. This was maybe the largest challenge of the Silence project. For the development an agile development method was chosen that involved the product owner during all the stages of the development. Terms were introduced like sprints, scrum meetings, back log, burn down charts, pigs and chickens. To keep the system up to date contracts were made between the product owner and the departments that supplied the data. This paper gives insight in the challenges and benefits of Silence, the a...

Prediction of travel time variability for cost-benefit analysis

Unreliable travel times cause substantial costs to travelers. Nevertheless, they are often not taken into account in cost-benefit analyses (CBA), or only in very rough ways. This paper aims at providing simple rules to predict variability, based on travel time data from Dutch highways. Two different concepts of travel time variability are used, which differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor. On average, longer delays are associated with higher variability. However, the derivative of variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic ‘regimes’ (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can then be used as an approximation.

Predicting Travel Time Variability for Cost-Benefit Analysis

Unreliable travel times cause substantial costs to travelers. Nevertheless, they are not taken into account in many cost-benefit-analyses (CBA), or only in very rough ways. This paper aims at providing simple rules on how variability can be predicted, based on travel time data from Dutch highways. The paper uses two different concepts of travel time variability. They differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of the day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor of variability. On average, longer delays are associated with higher variability. However, the derivative of travel time variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic 'regimes' (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can be used as an approximation.