With an increasing number of vehicles equipped with adaptive cruise control (ACC), the impact of such vehicles on the collective dynamics of traffic flow becomes relevant. By means of simulation, we investigate the influence of variable percentages of ACC vehicles on traffic flow characteristics. For simulating the ACC vehicles, we propose a new car-following model that also serves as the basis of an ACC implementation in real cars. The model is based on the intelligent driver model (IDM) and inherits its intuitive behavioural parameters: desired velocity, acceleration, comfortable deceleration and desired minimum time headway. It eliminates, however, the sometimes unrealistic behaviour of the IDM in cut-in situations with ensuing small gaps that regularly are caused by lane changes of other vehicles in dense or congested traffic. We simulate the influence of different ACC strategies on the maximum capacity before breakdown and the (dynamic) bottleneck capacity after breakdown. With a suitable strategy, we find sensitivities of the order of 0.3, i.e. 1 per cent more ACC vehicles will lead to an increase in the capacities by about 0.3 per cent. This sensitivity multiplies when considering travel times at actual breakdowns.

Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity

With an increasing number of vehicles equipped with adaptive cruise control (ACC), the impact of such vehicles on the collective dynamics of traffic flow becomes relevant. By means of simulation, we investigate the influence of variable percentages of ACC vehicles on traffic flow characteristics. For simulating the ACC vehicles, we propose a new car-following model that also serves as basis of an ACC implementation in real cars. The model is based on the Intelligent Driver Model [Treiber et al., Physical Review E 62, 1805 (2000)] and inherits its intuitive behavioural parameters: desired velocity, acceleration, comfortable deceleration, and desired minimum time headway. It eliminates, however, the sometimes unrealistic behaviour of the Intelligent Driver Model in cut-in situations with ensuing small gaps that regularly are caused by lane changes of other vehicles in dense or congested traffic. We simulate the influence of different ACC strategies on the maximum capacity before breakdown, and the (dynamic) bottleneck capacity after breakdown. With a suitable strategy, we find sensitivities of the order of 0.3, i.e., 1% more ACC vehicles will lead to an increase of the capacities by about 0.3%. This sensitivity multiplies when considering travel times at actual breakdowns.

Enhanced Intelligent Driver Model to Access the Impact of Driving Strategies on Traffic Capacity

This paper proposes a novel dynamic speed limit control model accounting for uncertain traffic demand and supply in a stochastic traffic network. First, a link based dynamic network loading model is developed to simulate the traffic flow propagation allowing the change of speed limits. Shockwave propagation is well defined and captured by checking the difference between the queue forming end and the dissipation end. Second, the dynamic speed limit problem is formulated as a Markov Decision Process (MDP) problem and solved by a real time control mechanism. The speed limit controller is modeled as an intelligent agent interacting with the stochastic network environment stochastic network environment to assign time dependent link based speed limits. Based on different metrics, e.g. total network throughput, delay time, vehicular emissions are optimized in the modeling framework, the optimal speed limit scheme is obtained by applying the R-Markov Average Reward Technique (R-MART) based reinforcement learning algorithm. A case study of the Sioux Falls network is constructed to test the performance of the model. Results show that the total travel time and emissions (in terms of CO) are reduced by around 18% and 20% compared with the base case of non-speed limit control.

Accounting for dynamic speed limit control in a stochastic traffic environment: a reinforcement learning approach

Basic reliability issues are addressed in this study, which is not concerned with average travel times, but rather ways of describing travel times that reflect the uncertainty in the amount of time required to travel between two points. Some of the uncertainty is systematic, such as the normal ebb and flow of traffic within the course of a work day or season of the year. Congestion associated with this systematic uncertainty is called recurrent. Congestion due to unpredictable or unexpected events is called nonrecurrent. If every travel time observed over a highway section for a year is plotted, a distribution of travel time is obtained. This plotted distribution is the picture of travel time variability. Such distributions are the focus of this research, especially the degree to which recurring and nonrecurring congestion influence the nature of the distribution. This research shows how to derive performance measures from such distributions and recommends a set for use by managers, planners, and systems operators. The research reexamines the composition of the congestion puzzle in terms of the fractions attributable to recurrent and various sources of nonrecurrent congestion. The project team used before-and-after studies to determine the effectiveness of different types of actions, both operational and capacity improvements, in improving reliability. This study also examined the effect of the downturn of the economy on travel time reliability. Finally, this research resulted in two types of models that can be used to estimate or predict travel time reliability. These models have broad applicability to planning, programming, and systems management and operations.

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Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies

A microsimulation calibration methodology based on matching speed—flow graphs from field and simulation is presented. Evaluation and automation of matching speed—flow graphs are based on methods from pattern recognition. The methodology was applied to the US-101 freeway network in San Francisco, California, by using an evolutionary algorithm. The methodology is compared to traditional methods of calibration based on capacity and is shown to perform better. In addition, a small-scale test-network simulation model was developed to assist in calibration of large-scale simulation models. The performance of the test-network-based calibration is comparable to the US-101 simulation model.

Microsimulation Calibration Using Speed-Flow Relationships

A stochastic concept for highway capacity analysis is presented. The capacity of a highway facility is regarded as a random variable instead of a constant value. Thus, the stochastic approach provides new measures of traffic flow performance based on aspects of traffic reliability. A method for the estimation of capacity distribution functions from empirical data based on statistical methods for lifetime data analysis is introduced. This method is derived for the analysis of freeway capacity. However, the stochastic approach also is shown to be applicable to intersections. Results of the analysis of data samples from freeway sections in Germany indicate that freeway capacity is Weibull-distributed with a considerable variance. A Monte Carlo technique based on the stochastic description of capacity is proposed to quantify freeway traffic performance over a whole year. This technique also provides a quantitative assessment for oversaturated conditions.

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Hendrik Zurlinden

Papers

Implementing the Concept of Reliability for Highway Capacity Analysis