Autonomous detection and anticipation of jam fronts from messages propagated by inter-vehicle communication

In this paper, a minimalist, completely distributed freeway traffic information system is introduced. It involves an autonomous, vehicle-based jam front detection, the information transmission via inter-vehicle communication, and the forecast of the spatial position of jam fronts by reconstructing the spatiotemporal traffic situation based on the transmitted information. The whole system is simulated with an integrated traffic simulator, that 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, we show that the algorithms for a congestion-front recognition, message transmission, and processing predict reliably the existence and position of jam fronts for vehicle equipment rates as low as 3%. A reliable mode of operation already for small market penetrations is crucial for the successful introduction of inter-vehicle communication. The short-term prediction of jam fronts is not only useful for the driver, but is essential for enhancing road safety and road capacity by intelligent adaptive cruise control systems.Comment: Published in the Proceedings of the Annual Meeting of the Transportation Research Board 200

Reconstructing the Traffic State by Fusion of Heterogeneous Data

We present an advanced interpolation method for estimating smooth spatiotemporal profiles for local highway traffic variables such as flow, speed and density. The method is based on stationary detector data as typically collected by traffic control centres, and may be augmented by floating car data or other traffic information. The resulting profiles display transitions between free and congested traffic in great detail, as well as fine structures such as stop-and-go waves. We establish the accuracy and robustness of the method and demonstrate three potential applications: 1. compensation for gaps in data caused by detector failure; 2. separation of noise from dynamic traffic information; and 3. the fusion of floating car data with stationary detector data.Comment: For more information see http://www.mtreiber.de or http://www.akesting.d

Utilizing shockwave theory and deep learning to estimate intersection traffic flow and queue length based on connected vehicle data.

The development of Connected Vehicles (CV) facilitates the use of trajectory data to estimate queue length and traffic volume at signalized intersections. The previously proposed models involved additional information that may require conducting different types of data collection. Also, most models need higher market penetration rate or more than a vehicle per cycle to provide adequate estimation. This is mainly because a significant number of the estimation models utilized only queued vehicles. However, the state of motion in non-queued vehicles, particularly slowed-down vehicles, provides much information about the traffic characteristics. There is a lack of exploiting the information from slowed-down vehicles in identifying the last queued vehicle to improve the estimation models. The importance of this work is to propose a cycle-by-cycle queue length and traffic volume estimation models by utilizing the slowed-down vehicles. It proposes a sophisticated model to estimate the queue length and traffic volume from connected vehicles with low market penetration rate (MPR) by utilizing shockwave theory and deep learning technique (artificial neural network). The work starts with establishing a relationship between the slowed-down vehicles and last queued vehicles based on shockwave theory and traffic flow dynamics. Then, the queue estimation algorithm is modeled based on the capacity state and deep learning technique. The traffic volume algorithm modeled is based on the queue length information. Four experiments were conducted to investigate the performance of the queue length and traffic volume estimation models on dataset from simulation environment and real-world data. The queue length results of the simulation experiment demonstrated an adequate mean absolute percentage error (MAPE) of 13.44%, which means an accuracy of 86.56%. The highest MAPE was 19.16% (80.84% accuracy) and the lowest was 7.36% (92.64%). The results of the queue length algorithm applied on real-world data demonstrated an MAPE of 21.97% (78.03% accuracy). The performance of the traffic volume algorithm on simulation data demonstrated an excellent MAPE of 11.8% (88.2% accuracy). The performance of the algorithm based on real-world data from demonstrated an MAPE of 23.57% (76.43% accuracy). Although the previous models can provide similar accuracy rates, they require higher MPR. With the low MPR of 10%, the proposed models revealed an adequate estimation accuracy of the cycle-by-cycle queue length and traffic volume

A Survey on platoon-based vehicular cyber-physical systems

Vehicles on the road with some common interests can cooperatively form a platoon-based driving pattern, in which a vehicle follows another one and maintains a small and nearly constant distance to the preceding vehicle. It has been proved that, compared to driving individually, such a platoon-based driving pattern can significantly improve the road capacity and energy efficiency. Moreover, with the emerging vehicular adhoc network (VANET), the performance of platoon in terms of road capacity, safety and energy efficiency, etc., can be further improved. On the other hand, the physical dynamics of vehicles inside the platoon can also affect the performance of VANET. Such a complex system can be considered as a platoon-based vehicular cyber-physical system (VCPS), which has attracted significant attention recently. In this paper, we present a comprehensive survey on platoon-based VCPS. We first review the related work of platoon-based VCPS. We then introduce two elementary techniques involved in platoon-based VCPS: the vehicular networking architecture and standards, and traffic dynamics, respectively. We further discuss the fundamental issues in platoon-based VCPS, including vehicle platooning/clustering, cooperative adaptive cruise control (CACC), platoon-based vehicular communications, etc., and all of which are characterized by the tight coupled relationship between traffic dynamics and VANET behaviors. Since system verification is critical to VCPS development, we also give an overview of VCPS simulation tools. Finally, we share our view on some open issues that may lead to new research directions

Impact of Connected Vehicles on Mitigating Secondary Crash Risk

Reducing the risk of secondary crashes is a key goal for effective traffic incident management. However, only few countermeasures have been established in practices to achieve the goal. This is mainly due to the stochastic nature of both primary and secondary crashes. Given the emerging connected vehicle (CV) technologies, it is highly likely that CVs will soon be able to communicate with each other through the ad-hoc wireless vehicular network. Information sharing among vehicles is deemed to change traffic operations and allow motorists for more proactive actions. Motorists who receive safety messages can be motivated to approach queues and incident sites with more caution. As a result of the improved situational awareness, the risk of secondary crashes is expected to be reduced. To examine whether this expectation is achievable or not, this study aims to assess the impact of connectivity on the risk of secondary crashes. A simulation-based modeling framework that enables vehicle-to-vehicle communication module was developed. Since crashes cannot be directly simulated in micro-simulation, the use of surrogate safety measures was proposed to capture vehicular conflicts as a proxy for secondary crash risk upstream of a primary crash site. An experimental study was conducted based on the developed simulation modeling framework. The results show that the use of connected vehicles can be a viable way to reduce the risk of secondary crashes. Their impact is expected to change with an increasing market penetration of connected vehicles. © 2017 Tongji University and Tongji University Press

Applicability and Application of Microscopic Traffic Simulations

Anwendbarkeit und Anwendung mikroskopischer Verkehrssimulationen Die Dynamik des Verkehrsflusses zeigt unterschiedliche, komplexe räumliche und zeitliche Muster. Ein bekanntes Phänomen ist beispielsweise der abrupte und plötzliche Abfall der Durchschnittsgeschwindigkeit auf einem Streckenabschnitt, der zu Stauungen führt. Zur Beurteilung der Qualität dreier ausgewählter Verkehrsmodelle (dem Nagel-Schreckenberg Modell (NSM), dem Intelligent Driver Model (IDM) und dem Comfortable Driving Model (CDM)) wird deren Fähigkeit einen solchen Zusammenbruch des Verkehrsflussses zu reproduzieren untersucht. Für eine möglichst realistische Untersuchung werden dazu reale Zählschleifendaten herangezogen, die eben einen solchen Zusammenbruch zeigen. Es werden mehrere Methoden vorgestellt, mit denen die übereinstimmung der empirischen mit den Simulationsdaten beurteilt werden kann. Zusätzlich wird der Einfluss einiger Modellierungsaspekte auf die Ergebnisse diskutiert. Für das CDM, welches eine gute übereinstimmung mit den Echtdaten zeigte, werden in einem zweiten Schritt die raum-zeitlichen Verkehrsmuster untersucht, die sich aus unterschiedlichen Ein- und Ausflussraten bei offenen Rändern ergeben. Auf Grundlage der Zeitreihen lokaler Messungen werden dann die Verkehrszustände den Verkehrsphasen der Kernerschen Drei-Phasen-Verkehrstheorie zugeordnet. Für diese Zuordnung wird die regelbasierte FOTO-Methode verwendet (Kerner et al., 2002). Auch diese Analyse zeigt, dass das CDM alle drei unterschiedlichen Verkehrsphasen (Freifluss, synchronisierter Verkehr und Stau) reproduzieren kann. Diese Beobachtung ist überraschend, da diese Eigenschaft von Verkehrsmodellen mit einem Fundamentaldiagramm wie dem CDM nicht erwartet wird. Aufgrund dieser insgesamt guten übereinstimmung mit empirischen Daten wurde das CDM auch für simulative Untersuchungen von Strategien zur Verkehrsflussoptimierung mittels Fahrzeug-Fahrzeug Kommunikation verwendet. Da der Zusammenbruch des Verkehrsflusses aus von Fahrern verursachten Störungen resultiert, wird vorgeschlagen die Verkehrslage durch regelmäßig gesendete Kurznachrichten zu analysieren und Fahrer vor einem drohenden Zusammenbruch des Verkehrs zu warnen. Fahrer, die eine solche Warnung erhalten, halten daraufhin einen größeren Abstand zum Vordermann ein und verursachen dadurch mit geringerer Wahrscheinlichkeit Fluktuationen, die den Verkehr zusammenbrechen lassen. Es zeigt sich, dass schon Durchdringungsraten von \SI{10}{\percent} kommunizierender Fahrzeuge einen deutlich messbaren Einfluss auf den gesamten Verkehrsfluss haben. Schließlich betrachten wir heterogenen Verkehr, bestehend aus kommunizierenden und nicht-kommunizierenden Fahrzeugen, genauer. Unter der Annhame, dass kommunizierende Fahrzeuge ihren Vorder- und Hintermann mit Hilfe von Sensoren orten können, fragen wir, wie viele nicht-kommunizierende Fahrzeuge so im Durchschnitt geortet werden.Traffic flow is a very prominent example of a driven non-equilibrium system, which shows a very complex spatiotemporal dynamics. A characteristic phenomenon of traffic dynamics is the spontaneous and abrupt drop of the average velocity on a stretch of road leading to congestion. To assess the quality of three selected microscopic traffic models (the Nagel-Schreckenberg model (NSM), the intelligent driver model (IDM), and the comfortable driving model (CDM)), we study their ability to reproduce such a traffic breakdown, whose spatiotemporal dynamics we investigate as well. Our analysis is based on empirical traffic data from stationary loop detectors showing a spontaneous breakdown on a German Autobahn. We then present several methods to assess the results and to compare the models with each other. In addition, we will also discuss some important modeling aspects and their impact on the resulting spatiotemporal pattern. For the CDM, which gave good results in this assessment, we analyze the spatiotemporal patterns resulting from different inflow and outflow rates with open boundary conditions. Based on time series of local measurements, the local traffic states are assigned to different traffic phases according to Kerner's three-phase traffic theory. For this classification we use the rule-based FOTO-method, which was developed by Kerner et al. in 2002. Our analysis shows that the model is indeed able to reproduce three qualitatively different traffic phases: free flow, synchronized traffic, and wide moving jams. This is surprising because traffic models with a fundamental diagram, such as the CDM, are not expected to reproduce the synchronized phase. By virtue of this overall good agreement with empirical findings, we chose the CDM to investigate via computer simulations how traffic congestion can be reduced with the help of vehicle-to-vehicle communication. As the reasons for a traffic breakdown are perturbations caused by human drivers in dense traffic, we propose using periodically emitted status messages to analyze traffic flow and to warn other drivers of a possible traffic breakdown. Drivers who receive such a warning are told to keep a larger gap to their predecessor. By doing so, they are less likely the source of perturbations, which can cause a traffic breakdown. We show that penetration rates of 10% and less can have significant influence on traffic flow and travel times. Finally, we address a rather practical problem of heterogeneous traffic consisting of communicating and non-communicating vehicles. If communicating vehicles can detect the vehicle ahead (and behind) by front (and rear) sensors, we give exact solutions for the average number of detected vehicles