Martin Schönhof

Empirical Features of Congested Traffic States and Their Implications for Traffic Modeling

We address the controversial issue of traffic flow modeling, whether first-order, second-order, or other traffic models are best supported by empirical facts and theoretical considerations. This is done by a critical discussion of the pros and cons of the different theoretical approaches and by the analysis of a large set of empirical data with new evaluation techniques. Specifically, we investigate characteristic properties of the congested traffic states on a 30-km-long stretch of the German freeway A5 near Frankfurt/Main. Among the approximately 245 breakdowns of traffic flow at several different bottlenecks in 165 days, we have identified five different kinds of spatiotemporal congestion patterns and their combinations. Based on an “adaptive smoothing method” for the visualization of detector data, we also discuss particular features of breakdowns, such as signs of unstable traffic flow and the “boomerang effect,” which often seems to be caused by overtaking maneuvers of trucks. Controversial issues such as “synchronized flow” or stop-and-go waves are addressed as well. Our empirical results are compared with the implications of different theoretical concepts such as first-order traffic models and the phase diagram of congested traffic states predicted by some second-order models and the nonlocal, gas-kinetic based traffic model (GKT model). For a correct understanding of empirical observations such as the “general pattern,” it is important to consider particularities such as the fact that off-ramps can act as bottlenecks, when activated by downstream on-ramp bottlenecks. As sequences of off-and on-ramps generate different congestion patterns than single on-ramps, they must be treated as interconnected bottlenecks. Furthermore, our empirical results question Kerners three-phase theory.

Theoretical vs. empirical classification and prediction of congested traffic states

Abstract Starting from the instability diagram of a traffic flow model, we derive conditions for the occurrence of congested traffic states, their appearance, their spreading in space and time, and the related increase in travel times. We discuss the terminology of traffic phases and give empirical evidence for the existence of a phase diagram of traffic states. In contrast to previously presented phase diagrams, it is shown that “widening synchronized patterns” are possible, if the maximum flow is located inside of a metastable density regime. Moreover, for various kinds of traffic models with different instability diagrams it is discussed, how the related phase diagrams are expected to approximately look like. Apart from this, it is pointed out that combinations of on- and off-ramps create different patterns than a single, isolated on-ramp.

Extending Adaptive Cruise Control to Adaptive Driving Strategies

An adaptive cruise control (ACC) strategy is presented in which acceleration characteristics, that is, driving styles, automatically adapt to different traffic situations. The three components of the concept are the ACC itself, implemented in the form of a car-following model; an algorithm for the automatic real-time detection of the traffic situation based on local information; and a driving strategy matrix to adapt the driving characteristics–that is, the parameters of the ACC controller–to the traffic conditions. As an option, intervehicle and roadside-to-car communication can be used to improve the accuracy for determining the local traffic states. The complete concept was simulated microscopically on a road section with an on-ramp bottleneck by using real loop-detector data for an afternoon peak period as input for the upstream boundary. A small percentage of traffic-adaptive ACC vehicles, a relatively modest local change in the maximum free flow, improves traffic stability and performance significantly. Although the traffic congestion in the reference case was completely eliminated when a proportion of 25% of ACC vehicles was simulated, travel times for the drivers were reduced in a relevant way for much lower penetration rates. The presented results are largely independent of details of the model, the boundary conditions, and the type of road inhomogeneity.

Volatile decision dynamics: experiments, stochastic description, intermittency control and traffic optimization

The coordinated and efficient distribution of limited resources by individual decisions is a fundamental, unsolved problem. When individuals compete for road capacities, time, space, money, goods, etc, they normally make decisions based on aggregate rather than complete information, such as TV news or stock market indices. In related experiments, we have observed a volatile decision dynamics and far-from-optimal payoff distributions. We have also identified methods of information presentation that can considerably improve the overall performance of the system. In order to determine optimal strategies of decision guidance by means of user-specific recommendations, a stochastic behavioural description is developed. These strategies manage to increase the adaptibility to changing conditions and to reduce the deviation from the time-dependent user equilibrium, thereby enhancing the average and individual payoffs. Hence, our guidance strategies can increase the performance of all users by reducing overreaction and stabilizing the decision dynamics. These results are highly significant for predicting decision behaviour, for reaching optimal behavioural distributions by decision support systems and for information service providers. One of the promising fields of application is traffic optimization.

Jam-Avoiding Adaptive Cruise Control (ACC) and its Impact on Traffic Dynamics

Adaptive-Cruise Control (ACC) automatically accelerates or decelerates a vehicle to maintain a selected time gap, to reach a desired velocity, or to prevent a rear-end collision. To this end, the ACC sensors detect and track the vehicle ahead for measuring the actual distance and speed difference. Together with the own velocity, these input variables are exactly the same as in car-following models. The focus of this contribution is: What will be the impact of a spreading of ACC systems on the traffic dynamics? Do automated driving strategies have the potential to improve the capacity and stability of traffic flow or will they necessarily increase the heterogeneity and instability? How does the result depend on the ACC equipment level?

Autonomous detection and anticipation of jam fronts from messages propagated by intervehicle communication

A minimalist, completely distributed freeway traffic information system is introduced. It involves autonomous, vehicle-based jam-front detection, information transmission via intervehicle communication, and forecast of the spatial position of jam fronts by reconstructing the spatiotemporal traffic situation on the basis of the transmitted information. The whole system is simulated with an integrated traffic simulator, which is based on a realistic microscopic traffic model for longitudinal movements and lane changes. The function of its communication module has been explicitly validated by comparing the simulation results with analytical calculations. By means of simulations, it is shown that the algorithms for congestion-front recognition, message transmission, and processing reliably predict the existence and position of jam fronts for vehicle equipment rates as low as 3%. A reliable mode of operation for small market penetrations is crucial for successful introduction of intervehicle communication. The short-term prediction of jam fronts is not only useful for the driver but also essential for enhancing road safety and road capacity by intelligent adaptive cruise control systems.

The German Autobahn: an ITS test bed for examining dynamic traffic flow phenomena

Traffic conditions were examined along a 30 km section of northbound Autobahn 5 near Frankfurt, Germany using archived inductive loop detector data. Fifteen bottleneck activations were identified and their reproducible features were described. Bottlenecks became active in the vicinity of on-ramps and off-ramps and once a bottleneck became active, its queue discharge flow was reproducible across multiple activations and across multiple days. The analysis tools used in this study were transformed curves of cumulative vehicle count and cumulative time-mean velocity. These cumulative curves provided the resolution necessary to reveal the spatial and temporal aspects of dynamic freeway traffic flow phenomena. With increasing availability of reliable freeway sensor data, it is important to continue the systematic empirical analysis of freeways in different countries with varying geometric configurations. The results of this research would assist with all aspects of traffic flow modeling, operations and control.

Toward Demonstrating Predictability of Bottleneck Activation on German Autobahns

Traffic conditions were examined along 30 km (18.6 mi) of northbound Autobahn 5 near Frankfurt, Germany, with archived inductive loop detector data recorded at 1-min intervals. With a focus on the spatiotemporal evolution of traffic between freely flowing and queued conditions, it was possible to identify 15 bottleneck activations and to characterize reproducible features related to their formation, discharge, and dissipation. This was done by systematically probing the excess vehicle accumulation (spatial) and travel time (temporal) that arose between measurement locations. Bottlenecks became active in the vicinity of on- and off-ramps. The evolution of a steady shock of low flow, low velocity, and relatively short duration was traced over 16 km (10 mi). Its cause is not known definitively, but some indications of its formation were revealed. Once a bottleneck became active, its measured outflow was reproducible across multiple activations and multiple days. The analysis tools used were transformed curve...

Modelling widely scattered states in ‘synchronized’ traffic flow and possible relevance for stock market dynamics

Traffic flow at low densities (free traffic) is characterized by a quasi-one-dimensional relation between traffic flow and vehicle density, while no such fundamental diagram exists for ‘synchronized’ congested traffic flow. Instead, a two-dimensional area of widely scattered flow-density data is observed as a consequence of a complex traffic dynamics. For an explanation of this phenomenon and transitions between the different traffic phases, we propose a new class of molecular-dynamics like microscopic traffic models based on times to collisions and discuss the properties by means of analytical arguments. Similar models may help to understand the laminar and turbulent phases in the dynamics of stock markets as well as the transitions among them.

Inter-Vehicle Communication on Freeways: Statistical Properties of Information Propagation in Ad-Hoc Networks

The function of adaptive cruise control (ACC) systems can be enhanced by information flows between equipped cars, i.e., by upstream transmission of messages about the current traffic situation. Message transport within one driving direction is obviously rather restricted for small percentages of equipped cars due to the limited broadcast range. Thus, we consider vehicles in the opposite driving direction as possible relay stations. Analytical results based on a Poisson approximation, which are in accordance with empirical traffic data, show the efficiency and velocity of information propagation based on transversal message hopping. The obtained propability distributions of the transmission times are compared with numerical results of microscopic traffic simulations. By simulating the formation of a typical traffic jam, we show how information about distant bottlenecks and jam fronts reaches upstream equipped cars, which then can optimize their driving strategies.