B. Van Arem

Energy efficient traffic management and control - the eCoMove approach and expected benefits

Transportation is an important contributor to environmental problems like declining air quality. Current traffic measures seek to optimize travel times, but fall short on the ideal of an ecologically friendly traffic system. The European research project eCoMove aims to reduce the overall fuel consumption in traffic by 20 percent by means of energy efficient driving behavior and energy efficient traffic management and control. This paper contributes to the understanding of the feasibility of this goal. Vehicle-to-vehicle and vehicle-to-infrastructure communication is a key element in the vision of eCoMove. In this paper the eCoMove concepts are presented by means of an in-depth look on the eco Adaptive Balancing and Control system. Preliminary studies on rerouting, green priority and speed advice at signalized intersections indicate signification environmental benefits.

A human factors perspective on automated driving

ABSTRACT Automated driving can fundamentally change road transportation and improve quality of life. However, at present, the role of humans in automated vehicles (AVs) is not clearly established. Interviews were conducted in April and May 2015 with 12 expert researchers in the field of human factors (HFs) of automated driving to identify commonalities and distinctive perspectives regarding HF challenges in the development of AVs. The experts indicated that an AV up to SAE Level 4 should inform its driver about the AVs capabilities and operational status, and ensure safety while changing between automated and manual modes. HF research should particularly address interactions between AVs, human drivers and vulnerable road users. Additionally, driver-training programmes may have to be modified to ensure that humans are capable of using AVs. Finally, a reflection on the interviews is provided, showing discordance between the interviewees’ statements – which appear to be in line with a long history of HFs research – and the rapid development of automation technology. We expect our perspective to be instrumental for stakeholders involved in AV development and instructive to other parties.

Gas-Kinetic Traffic Flow Modeling Including Continuous Driver Behavior Models

A modeling technique is presented that analytically bridges the gap between microscopic behavior of individual drivers and the macroscopic dynamics of traffic flow. The basis of this approach is the (gas-) kinetic or mesoscopic modeling principle that considers the dynamics of traffic density and generalizations thereof as a probability density function of vehicles in different driving states. In contrast to traditional kinetic models, deceleration of individual vehicles due to slower traffic is treated as a continuous adaptive process rather than a discrete event. An analytic procedure is proposed to aggregate arbitrarily refined individual driver behavior to a macroscopic expected acceleration or deceleration of flow as a whole that can be used in macroscopic differential equations for traffic flow. The procedure implicitly accounts for the anisotropy of information flow in traffic, for anticipation behavior of drivers, and for the finite space requirement of vehicles, as long as these properties have been specified at the level of individual driver behavior. The procedure is illustrated for a simple car-following model with overtaking opportunity. The results show that the procedure yields micro-based aggregate traffic flow models that capture the essential properties of traffic dynamics. The techniques presented can contribute to the development of traffic flow models with driver behavior and driver psychology as important explanatory factors of congestion formation and propagation. Moreover, the approach allows building macroscopic traffic flow equations from future traffic flows for which no empirical speed–flow–density relations are available yet.

A cooperative system based variable speed limit control algorithm against jam waves - an extension of the SPECIALIST algorithm

The SPECIALIST algorithm can resolve jam waves on freeways using roadside technology: detector loops and speed limit gantries. In this paper we extend the algorithm, enabling the integration with cooperative system technologies and other road side detectors, such as in-car detection and actuation, and video-based monitoring (VBM). Integrating these cooperative elements can provide faster and more accurate jam detection, which leads to a better performance of the SPECIALIST algorithm. For the fusion of the various data sources (loops, VBM, floating car data) an extension of the Adaptive Smoothing Method is used. The data fusion method is also adapted to comply with the input requirements of the SPECIALIST algorithm. The resulting system is suitable for a mixed roadside/in-car detection and actuation environment. The performance of the resulting system is evaluated using the microscopic simulator VISSIM. The results show that floating car data and VBM can considerably improve the jam detection times and the accuracy of the detected jam location, which lead to more efficient speed limit schemes.

Mainstream traffic flow control at sags

Sags are freeway sections along which the gradient changes significantly from downward to upward. The capacity of sags is considerably lower than the capacity of normal sections. Consequently, sags are often freeway bottlenecks. Recently, several control measures have been proposed to improve traffic flow efficiency at sags. Those measures generally aim to increase the capacity of the bottleneck, to prevent traffic flow perturbations in nearly saturated conditions, or both. This paper presents an alternative type of measure based on the concept of mainstream traffic flow control. The proposed control measure regulates traffic density at the bottleneck area to keep it below the critical density and hence prevent traffic from breaking down while maximizing outflow. Density is regulated by means of a variable speed limit section that regulates the inflow to the bottleneck. Speed limits are selected on the basis of a feedback control law. The authors evaluate the effectiveness of the proposed control strategy by means of a simple case study by using microscopic traffic simulation. The results show a significant increase in bottleneck outflow, particularly during periods of high demand, which leads to a considerable decrease in total delay. This finding suggests that mainstream traffic flow control strategies that use variable speed limits have the potential to improve substantially the performance of freeway networks containing sags.

Driver assistance systems modeling by model predictive control

Recently, an optimal control framework has been put forward to model human driver behavior and Advanced Driver Assistance Systems (ADAS). Although the models are shown to be face valid, applications are limited to the openloop optimal control problem and assume that a driver expects a certain dynamic speed profile of his predecessor. While human drivers are assumed to be good in anticipating traffic, ADAS are poor at predicting the dynamics of other vehicles, due to limited looking-ahead range and errors of sensors and actuators. This contribution furthers the optimal control framework to model automatic ADAS vehicles by relaxing the assumption that ADAS vehicles can predict the dynamics of other vehicles and solving the optimal control problem in a receding horizon way. This work assumes that within a prediction horizon, other vehicles are driving at stationary conditions (zero accelerations). An ADAS vehicle predicts system dynamics based on this assumption and makes control decisions to optimize its cost. The modeling framework is applied to design ACC systems and EcoACC systems, where multiple control objectives including safety, efficiency and sustainability are taken into account. The prediction horizon is tuned by face validating the behavior of a controlled vehicle. Simulation comparisons show that EcoACC systems result in higher fuel efficiency and a smoother vehicular behavior compared to ACC systems, although this depends on the exact formulation of the objectives.

Delay-Compensating Strategy to Enhance String Stability of Adaptive Cruise Controlled Vehicles

ABSTRACT A novel strategy to enhance string stability of autonomous vehicles with sensor delay and actuator lag is proposed based on a model predictive control framework. To compensate sensor delay, the approach entails estimating the (unknown) system state at the current time using the system state in a previous time, the applied control history and a system dynamics model. The actuator lag is compensated by including the lag in the state prediction model. The mathematical framework shows that without the anticipation strategy, sensor delay leads to a worse estimate of the initial condition for the optimal control problem and actuator lag increases the mismatch between the system state prediction model and the actual system behaviour. Simulation verified that sensor delay and actuator lag degrade string stability of platoons. The proposed anticipatory control strategy shows clear benefits in improving autonomous vehicle string stability and hence has potential to enhance traffic flow stability.

Delays caused by incidents. Data-driven approach

Road networks are becoming more vulnerable to incidents as a result of the increasing level of congestion. An innovative empirical method is proposed and applied to analyze the delays caused by about 490,000 incidents that occurred in the Netherlands in the period 2007 to 2009. The method was applied to the motorway network of the Netherlands for which detailed loop detector data were available and for which incidents were registered. The method contributes to the existing literature by explaining how delays that are caused by incidents can be computed by selecting a reference day and by tracing the effects of incidents. The method considers upstream delays on the road where the incident occurred, spillback effects to other roads, and rubbernecking effects. The method provides insight for the locations that are most vulnerable to incidents and a basis for taking robustness measures and understanding the potential benefits of these measures.

Reducing congestion at uphill freeway sections by means of a Gradient Compensation System

Uphill sections have often been identified as capacity bottlenecks in freeway networks. One of the main reasons seems to be that drivers reduce speed when they reach the beginning of an uphill section. With high traffic demand, the deceleration of the first vehicle of a platoon can generate a flow disturbance that amplifies as it propagates upstream, triggering the formation of a traffic jam. This paper presents a proof of concept by exploring whether equipping the leader of a platoon with an in-vehicle Gradient Compensation System (GCS) can improve traffic flow efficiency on uphill sections. The GCS assists the driver in performing the longitudinal driving task at uphill sections. We present the results of a series of traffic simulation experiments in which a platoon of vehicles drive on a single-lane freeway stretch containing an uphill section. The phenomenon of speed reduction is modeled by means of a sub-microscopic traffic simulation program. The results show that if the platoon leader is not equipped with the GCS, its speed drop at the beginning of the uphill section can cause a traffic breakdown, as observed in reality. However, if the platoon leader is equipped with the GCS, the magnitude of the speed drop is reduced, preventing congestion formation.

Investigating the effects of improving public transport system linkage to spatial strategy on controlling urban sprawl: evidence from Surabaya City, Indonesia

The phenomenon of sprawl has been a huge issue since the beginning of the 20th century and is characterized by rapid and unbalanced settlement development, with transportation network, particularly in the suburban areas. Academic research has explained the linkage strategy between transportation network and urban planning. However, insufficient empirical verification has been carried out to reduce this phenomenon by using the integrated approach of space-transport development. This paper focuses on analyzing the improvement of public transport supply incorporated in the settlement development. The improvement of public transport (PT) is designed by planning Mass Rapid Transit (MRT), Light Rapid Transit (LRT), Bus Rapid Transit (BRT) and feeder systems. The impact of PT improvement has an effect on the settlement development. In addition, creating a balance between employment and population density is designed as an alternative to urban spatial strategy. These approaches are necessary in order to analyze and to evaluate the many alternatives proposed as a solution to overcome this phenomenon. The conclusions reveal that the requirement for linkage space-transport development strategy in order to control settlement in the suburbs has to involve reduction of 35% in travel time and to increase doubling of the use of PT.