S.C. Calvert

Modelling cooperative driving in congestion shockwaves on a freeway network

The development of advanced driver assistance technology continues to proceed rapidly. Cooperative systems based on wireless communication are a specific form of advanced driver assistance that is currently evolving rapidly. A drawback in the development of such systems is that options for large scale field-testing and — development of these automated systems are limited. Traffic simulation however offers widespread options for testing. In this paper the effects of cooperative driving using cooperative adaptive cruise control (CACC) to influence congestion shockwaves are evaluated on a part of the Amsterdam freeway network. The effects of congestion shockwaves on a network scale can be different to uniform freeway sections due to interaction between varying traffic flows. The application of CACC to mitigate the negative effects of shockwaves on a network level are simulated and analysed in this research for varying levels of CACC penetration. The results are analysed on both a quantitative as well as qualitative level and give a deeper understanding into the possibilities of the mass application of CACC systems.

Influence of rain on motorway road capacity - A data-driven analysis

The influence of rain on motorways is proven degradative for traffic flow. This degradation can be seen from lower capacity and speed values. The effect of rain is however a highly stochastic one. Although relationships between rain intensity and capacity reduction have been offered, these show a large spread in quantitative outcomes. In this contribution a large data-driven analysis is performed for the capacity reduction effects of rain on Dutch motorways. This is performed for 11 different motorways at multiple locations per motorway and over two years resulting in 6550 capacity distribution observations. In comparison to previous research, this contribution gains strength from the large number of locations and highly accurate traffic and detected precipitation data. The analysis proves a statistically significant decrease in road capacity with increasing rain intensity and found a relationship in degradation of capacity of 1.9% per mm intensity of rainfall.

A methodology for road traffic resilience analysis and review of related concepts

ABSTRACT Major and minor disturbances can have a considerable impact on the performance of road networks. In this respect, resilience is considered as the ability of a road section to resist and to recover from disturbances in traffic flow. In this contribution, an indicator is presented, the Link Performance Index for Resilience (LPIR), which evaluates the resilience level of individual road sections in relation to a wider road network. The indicator can be used to detect poorly resilient road sections and to analyse which underlying road and traffic characteristics cause this non-resilience. The method adds to related concepts such as robustness and vulnerability by also considering recovery from congestion events explicitly and by focussing on everyday operational traffic situations rather than just on disasters or major events. The LPIR is demonstrated in an experimental case on a real network in which the effectiveness of the method is demonstrated.

Cooperative driving in mixed traffic networks — Optimizing for performance

This paper discusses a cooperative adaptive cruise control application and its effects on the traffic system. In previous work this application has been tested on the road, and traffic simulation has been used to scale up the results of the field test to larger networks and more vehicles. The present study investigates the dependence of the traffic impact of the time headway settings of the application and on its penetration rate. It will be shown both theoretically and empirically that traffic flows and road capacities will improve significantly with the fraction of equipped vehicles, and that this improvement depends on the configuration of the application.

Bounded Acceleration Capacity Drop in a Lagrangian Formulation of the Kinematic Wave Model with Vehicle Characteristics and Unconstrained Overtaking

In this contribution a model-based analysis of the application of bounded acceleration in traffic flow is considered as a cause for the capacity drop. This is performed in a Lagrangian formulation of the kinematic wave model with general vehicle specific characteristics. Unconstrained overtaking is presumed, which allows a demonstration to be given of the influence that constraints in traffic flow may have on the capacity drop. An experimental case demonstrates that bounded acceleration in traffic flow with unconstrained overtaking has very limited effect on the capacity drop. This implies that the capacity drop when incurred through bounded acceleration must make use of (semi-)constrained traffic flow, in which variety in vehicle acceleration ability may also be required to increase inhomogeneity. This is an important conclusion as it further defines the conditions required for capacity drop. The application of a Lagrangian formulation with advection invariant combined with bounded acceleration is also novel. The contribution further shows that the application of bounded acceleration in the presented model is feasible, although adjustments are required to capture the capacity drop through bounded acceleration.

A hybrid travel time prediction framework for planned motorway roadworks

In this paper we propose a hybrid motorway travel time prediction framework aimed at providing pre-trip travel information in case of roadworks. The framework utilises a first order macroscopic traffic flow model to predict the consequences in travel time of changes in both traffic demand and roadway capacity. Data-driven approaches are used to estimate both demand and capacity. From a large database of detailed loop detection data the most likely demand profiles are estimated for the considered workzone location, taking into account the possible effects of mobility management. Capacity estimation techniques are employed which combine historical data with likely capacity reduction factors as a consequence of the roadworks. On the basis of evaluation on a workzone case on a densely used 20 km stretch of a three lane motorway in The Netherlands, it is demonstrated that the proposed approach is able to predict travel time within a 20% margin in over 96% of all cases. The proposed approach is suitable for trip planning purposes (e.g. route planners and travel information websites), and may also be utilised by the road authorities in the actual planning of the roadworks.

Will automated vehicles negatively impact traffic flow

With low-level vehicle automation already available, there is a necessity to estimate its effects on traffic flow, especially if these could be negative. A long gradual transition will occur from manual driving to automated driving, in which many yet unknown traffic flow dynamics will be present. These effects have the potential to increasingly aid or cripple current road networks. In this contribution, we investigate these effects using an empirically calibrated and validated simulation experiment, backed up with findings from literature. We found that low-level automated vehicles in mixed traffic will initially have a small negative effect on traffic flow and road capacities. The experiment further showed that any improvement in traffic flow will only be seen at penetration rates above 70%. Also, the capacity drop appeared to be slightly higher with the presence of low-level automated vehicles. The experiment further investigated the effect of bottleneck severity and truck shares on traffic flow. Improvements to current traffic models are recommended and should include a greater detail and understanding of driver-vehicle interaction, both in conventional and in mixed traffic flow. Further research into behavioural shifts in driving is also recommended due to limited data and knowledge of these dynamics.

Exploring the Effects of Perception Errors and Anticipation Strategies on Traffic Accidents - A Simulation Study

It is remarkable that drivers (on average) can safely navigate through dense traffic at high speeds—conditions in which the time headways between vehicles are in the same order of magnitude as human reaction times. One explanation for this is the ability of drivers to anticipate on the traffic conditions in their surroundings. In this paper, we study, through simulation, the effects of reaction times, errors in perception and anticipation on the probability of accidents on freeways. To this end we extend an existing model for car following and lane changing with a perception and anticipation model inspired by Ensley’s three levels of situational awareness (perception, understanding and projection). By systematically varying driving behavior with different reaction times over a range of perception errors, and anticipation strategies, we compute efficiency effects (capacity and total time spent) and safety effects (the probability density of accidents happening as a function of these different contributing factors and errors). The results provide some evidence that safe driving is robust with respect to perception errors under simple anticipation strategies and small reaction times. When reaction times grow larger, more advanced anticipation strategies are needed to guarantee safe driving.

Real-time travel time prediction framework for departure time and route advice

Heavily used urban networks remain a challenge for travel time prediction because traffic flow is rarely homogeneous and is also subject to a wide variety of disturbances. Various models, some of which use traffic flow theory and some of which are data driven, have been developed to predict traffic flow and travel times. Many of these perform well under set conditions. However, few perform well under all or even most urban traffic conditions. As part of the Amsterdam Practical Trial, a comprehensive field operation test for traffic management, a real-time travel time prediction framework, was developed to make use of an ensemble of traffic modeling techniques to predict travel times with great accuracy for arterial roads as well as urban roads. The various models in the framework include both traffic theoretical models and data-driven approaches, making use of some of the largest real-time traffic data sets currently available to limit errors to less than 20% for any time of day or week. The impending implementation of the framework sets it at the forefront of practical real-time implementation of urban travel time prediction.