Search

Traffic safety is arguably the most important traffic metric from a human perspective. Still, many millions of people are killed on roads every year. In this paper, the concept of Herd Immunity for Traffic Safety (HITS) is presented for the first time. This concept focuses on identifying and describing the increased level of safety that is achieved when Connected Automated Vehicles (CAV) and Human Driven Vehicles (HDV) co-exist in mixed traffic. The underlying mechanism is described with a key component being the ability of CAVs to absorb human error and reduce exposure to risk. With increasing levels of CAV penetration, so-called tipping points occur in which the traffic safety grows in proportion to the penetration rate, which is demonstrated by the non-linearity of the penetration–risk relationship. This is demonstrated in theory and experimental cases while requirements to understand and apply the concept more extensively in the future are presented.

A Field-Operational-Test (FOT) with CACC vehicles and wireless communication with intelligent intersections on arterial roads was performed in autumn 2018, initiated by the Province of Noord-Holland in The Netherlands. The goal of the pilot was to demonstrate the feasibility of CACC platooning and V2I/I2V (infrastructure-to-vehicle) communication and investigate the potential effects on traffic flow and safety in such an urban environment. During the pilot, seven CACC-enabled vehicles traversed a provincial road corridor, crossing five intelligent intersections, sending Cooperative Awareness Message (CAM) and receiving time-to-green information from Intelligent Traffic Signals (iTS). A simulation model was calibrated using the data from the pilot to give indications of the potential future effects of CACC and I2V communication. The results showed that full CACC penetration can lead to 5% lower travel times on average with an enhanced improvement when intelligent intersections with V2I/I2V technology is applied (11% lower). If scaled up to the provincial road network, this could lead to a reduction of 12% in the total experienced delay for road users. As for previous tests on motorways, a reduction in effectiveness was also found for lower CACC- penetration rates with penetration rates of 10% no longer showing any substantial gains.

Is it possible to use just aggregate carriageway data for the evaluation of congestion warning systems (CWS) in large networks—or any system affecting traffic safety for that matter? In this paper, two hypotheses related to this question are tested. The first hypothesis is that it can be done by comparing large-scale congestion patterns on road stretches with and without CWS. The underlying rationale is that heterogeneous congestion patterns with many disturbances, frequent wide moving jams, and large speed differences result in more potentially unsafe traffic conditions than more homogeneous congestion patterns. The second hypothesis is that it is possible to compare differences in average (maximum) deceleration distributions into congestion waves between road stretches with and without CWS. Both hypotheses have been tested for similar bottlenecks with similar demand patterns and the results suggest the first hypothesis must be rejected. Although the idea seems plausible (CWS result in more homogeneous congestion patterns) there were too many confounding factors in the data to make the case. However, persuasive evidence was found for the second hypothesis. Statistically significant differences were found between (maximum) deceleration distributions on road stretches with and without CWS that suggest CWS do—as expected—contribute positively to traffic safety. It thus seems to be possible to monitor safety effects using just average speeds. However, the method is limited to providing relative comparisons. Furthermore, to fully rule out the effects of unobserved factors, more evidence and validation with microscopic data are needed.

With automated and cooperative driving making its breakthrough, and related systems in fast development, their future influence and impact on roads and traffic may be extensive. Truck platooning is such an application that relies on the development of Cooperative Adaptive Cruise Control (CACC) and is said to be practice ready. While the main advantages of truck platooning lie in emission and energy reduction, claims are also being made for the influence on traffic flow. In this paper, we pose hypotheses based on some of the main claims. We also attempt to substantiate and give quantitative proof of the potential effects of truck platooning on traffic flow performance. The simulation model LMRS-IDM+ is extended to encompass the main influencing dynamics related to potential effects of truck platooning, based on empirical findings. The effects of truck platooning were tested for the influence of traffic states, truck gap settings, platoon sizes, and the share of equipped trucks. This resulted in outcomes regarding the total traffic performance, the performance of traffic at ramps, and the ability of a platoon to remain platooning as part of a case experiment performed on a part of the Trans-European ITS Corridor. The results showed that truck platooning may have a small negative effect on the total non-saturated traffic flow, however with a much larger negative effect on saturated traffic flow. However, drivers may be reluctant to platoon in saturated traffic in any case. The ability of inflowing traffic to merge at on-ramps was found to be affected by truck platoons, with platoon disengagements occurring under various conditions. The applied gap settings for platooning trucks did not significantly affect the merge time, while a higher gap did lead to a higher number of disengagements. The ability of trucks to platoon was positively affected by a greater percentage of equipped trucks and by larger platoon sizes. Shorter gap times also slightly improved the ability of trucks to remain in platooning formation. Finally, recommendations are given to improve platoon strategies and for policymakers to only allow truck platooning outside of busy (near-) saturated traffic, even though drivers may be reluctant to use the system in these conditions. Also, recommendations are made to investigate potential differences in the effects between the European and American contexts for truck-platooning.

Highway bottlenecks are responsible for the majority of traffic congestion. Although the problem of bottleneck detection is not new, contemporary methods have not solved the problem thoroughly with regards to bottleneck locations, activation time, and related congestion tracking. These elements are essential for identifying and characterizing a bottleneck. This paper proposes a comprehensive framework for detecting and extracting these features of highway bottlenecks from traffic data. We particularly focus on questions (i) whether a bottleneck is the primary source of congestion or (ii) whether it is activated due to congestion caused by another downstream bottleneck. The underlying principles of the proposed method include the detection of congestion (in spatio-temporal patterns of traffic congestion), and the detection of speed discontinuities in traffic data (since this is an important indicator of a bottleneck activation). The method is data-driven and automatic therefore can be easily applied to different highways and used to obtain meaningful statistics of existing bottlenecks. We have tested the method on simulated data and also demonstrated it on real data from a busy highway section in the Netherlands. The results suggest that the method is robust to different implementations, i.e. locations, of loop-detectors which measure traffic at discrete locations.

Classification of congestion patterns is important in many areas in traffic planning and management, ranging from policy appraisal, database design, to prediction and real-time control. One of the key constraints in applying machine learning techniques for classification is the availability of sufficient data (traffic patterns) with clear and undisputed labels, e.g. traffic pattern X or Y. The challenge is that labelling traffic patterns (e.g. combinations of congested and freely flow areas over time and space) is highly subjective. In our view this means that assessment of how well algorithms label the data should also include a qualitative component that focuses on what the found patterns really mean for traffic flow operations and applications. In this study, we investigate the application of clustering analysis to obtain labels automatically from the data, where we indeed first qualitatively assess how meaningful the found labels are, and subsequently test quantitatively how well the labels separate the resulting feature space. By transforming traffic measurements (speeds) into (colored) images, two different approaches are proposed to extract the features of a large number of traffic patterns for clustering: point-based and area-based. The point-based approach is widely applied in the image processing literature, and explores local interest points in images (i.e. where large changes occur in color intensity); whereas a new area-based approach combines domain knowledge with Watershed segmentation to partition the images into different spatial-temporal segments from which domain specific features, such as wide moving jam patterns, are extracted. The results show that the Watershed segmentation separates the traffic (congestion) patterns into more meaningful and separable classes, comparable to those that have been proposed in the literature. Since there is no ground-truth set of labels, the quantitative assessment tests how well both methods are able to separate the respective feature spaces they construct for the (large) database of traffic patterns. We argue that the more crisp this separation is; the better the labelling has turned out. For this quantitative comparison we train a multinomial classifier that maps unseen patterns to the labels discovered by each of the two labeling approaches. The most important result is that the classifier using the area-based feature vector achieves the highest average levels of confidence in its decisions to classify patterns, implying a highly separable feature vector space. We argue this is good news! Not only does the combination of image processing (Watershed) and domain knowledge (traffic flow characteristics) lead to meaningful labels that can be automatically retrieved from large databases of data; this method also leads to a more efficient separation of the resulting feature space. Our next endeavor is to further refine and use this method to develop a search engine for the (rapidly growing) 200 TB historical database of traffic data hosted by the Dutch National Datawarehouse (NDW).

Incorporation of more sophisticated human factors (HF) in mathematical models for driving behavior has become an increasingly popular and important research direction in the last few years. Such models enable us to simulate under which conditions perception errors and risk-taking lead to interactions that result in unsafe traffic conditions and ultimately accidents. In this paper, we present a generic multi-level microscopic traffic modelling and simulation framework that supports this important line of research. In this framework, the driving task is modeled in a multi-layered fashion. At the highest level, we have idealized (collision-free) models for car following and other driving tasks. These models typically contain HF parameters that exogenously “govern the human factor”, such as reaction time, sensitivities to stimuli, desired speed, etc. At the lowest level, we define HF variables (task demand and capacity, awareness) with which we maintain what the information processing costs are of performing driving tasks as well as non-driving related tasks such as distractions. We model these costs using so-called fundamental diagrams of task demand. In between, we define functions that govern the dynamics of the high-level HF parameters with these HF variables as inputs. When total task demand increases beyond task capacity, first awareness may deteriorate, where we use Endsley's three-level awareness construct to differentiate between effects on perception, comprehension, anticipation and reaction time. Secondly, drivers may adapt their response in line with Fullers risk allostasis theory to reduce risk to acceptable levels. This framework can be viewed as a meta model, that provides the analyst possibilities to combine and mix a wide variety of microscopic models for driving behavior at different levels of sophistication, depending on which HF are studied, and which phenomena need to be reproduced. We illustrate the framework with a distraction (rubbernecking) case. Our results show that the framework results in endogenous mechanisms for inter- and intra-driver differences in driving behavior and can generate multiple plausible HF mechanisms to explain the same observable traffic phenomena and congestion patterns that arise due to the distraction. We believe our framework can serve as a valuable tool in testing hypotheses related to the effects of HF on traffic efficiency and traffic safety in a systematic way for both the traffic flow and HF community.

The capacity drop forms a major reason why the prevention of congestion is targeted by traffic management, as lower capacities are detrimental to traffic throughput. Various reasons describing the dynamics behind the capacity have been described, however one of these, reaction times, has had less explicit attention when modelling on a macroscopic flow level. In this contribution, a method to include the effect of reaction times for the capacity drop in heterogeneous traffic is proposed. The applied method further overcomes difficulties in including reaction times in a discrete time model through relaxation of the updating process in the discretization. This approach is novel for application in the considered first order approach, which is practise ready, contrary to many other models that propose similar approaches. The combination of the introduced method and the model form a solid development and method to apply the capacity drop based on this causation of the capacity drop. The results of the experiment case showed that the influence of traffic heterogeneity had a limited effect on the severity of the capacity drop, while it did influence the time of congestion onset. The influence of the reaction time on traffic showed greater capacity drop values for greater reaction time settings. The findings showed the method effective and valid, while the model application is also practise ready. Keywords: Capacity drop, Traffic modelling, Heterogeneous traffic, Traffic flow

Item does not contain fulltext As automated vehicles become increasingly common on the road, the call for an appropriate preparation for its drivers is becoming more urgent. Expert opinions and insights have been acquired via a focus group discussion with eleven Dutch driving examiners to assist in inventorying what types of preparations are needed. The concept of meaningful human control (MHC) as an integral part of the discussion lead to consensual findings regarding ADAS functionality and the drivers’ tasks, as well as discussion topics on driver training and levels of automation. It was concluded to have more research into human factors to safeguard proper control over automated vehicles. 2020 Forum on Integrated and Sustainable Transportation Systems (FISTS) (Delft, the Netherlands. 3-5 Nov. 2020)

New smart traffic signal control algorithms are capable of predicting the traffic signal state (red, green, or amber) changes, which can be provided to users to achieve more efficient traffic flow. However, these predictions pose an uncertain impact on the traffic flow and safety depending upon the quality of prediction. The information regarding the current state as well as the predicted residual time of state is communicated to other users in the form of Signal Phase and Timing (SPaT) data. In this paper, the SPaT message data is analyzed from an on-road pilot of different traffic signal control algorithms on provincial roads in the Province of North Holland. For analysis, new methods and indicators for quantification of prediction accuracy and quality of algorithm are proposed. These indicators can either be used for correction of state change prediction in real-time or for comparative analysis of the performance of different traffic signal control algorithms. This paper presents three main findings. First, it is found that a half fixed algorithm has very high prediction accuracy up to 99% for optimized directions. Second, the prediction accuracy of Time to Amber predictions improved by around 30% with this algorithm. Third, the overall reliability of prediction always increased with the use of the algorithm.

The inclusion of human factors (HF) in mathematical models is proving crucial to allow complex driving behaviour and interactions to be explicitly considered to capture driving phenomena. An important area where such integration is required is for the role of anticipation by drivers to compensate for critical traffic situations. In this paper, we introduce the concept of Anticipation Reliance (AR), which acts as a demand lowering compensative effect for the driving task by relying more on anticipation. We implement AR in a generic multi-scale microscopic traffic modelling and simulation framework to explore and explain the effects of HF on traffic operations and safety in critical traffic situations. This concept addresses a disparity in the description of driver workload in relation to the execution of driving tasks in regard to the confidence that drivers place on tasks that are sub-consciously catered for. The crossover from HF to a mathematical description of this role of AR introduces a ground-breaking concept that explains and models the mechanisms that allow drivers to compensate and avoid accidents in many circumstances, even when driving errors or sub-optimal driving performance occurs. By and large, the HF effects can be subdivided in effects on perception and anticipation; effects on sensitivity and response to stimuli; and effects on personal attributes and characteristics. A key aspect of the framework are two intertwined driver-specific mental state variables—total workload and awareness—that bridge between classic collision-free idealized models for lane changing and car following, and HF models that explain under which conditions the performance of drivers deteriorates in terms of reaction time, sensitivity to stimuli or other parameters. In this paper, we focus on the awareness construct, as described by AR, and explore the effects. We prove the effectiveness of the approach with a case example that demonstrates the ability of the model to dissect a complex traffic situation with both longitudinal and lateral driving tasks, while endogenously considering human factors and that produces accident avoidance and occurrence within the same order of magnitude compared to real traffic accident statistics.