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«FRIEND: A CYBER-PHYSICAL SYSTEM FOR TRAFFIC FLOW RELATED INFORMATION AGGREGATION AND DISSEMINATION by Samy S. El-Tawab B.S. June 2002, Alexandria ...»

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FRIEND: A CYBER-PHYSICAL SYSTEM FOR TRAFFIC FLOW

RELATED INFORMATION AGGREGATION AND

DISSEMINATION

by

Samy S. El-Tawab

B.S. June 2002, Alexandria University, Egypt

M.S. June 2006, Alexandria University, Egypt

A Dissertation Submitted to the Faculty of

Old Dominion University in Partial Fulfillment of the

Requirements for the Degree of

DOCTOR OF PHILOSOPHY

COMPUTER SCIENCE

OLD DOMINION UNIVERSITY

August 2012

Approved by:

Stephan Olariu (Director) Hussein Abdel-Wahab (Member) Michele C. Weigle (Member) Tamer Nadeem (Member) Dimitrie C. Popescu (Member) ABSTRACT

FRIEND: A CYBER-PHYSICAL SYSTEM FOR TRAFFIC FLOW

RELATED INFORMATION AGGREGATION AND DISSEMINATION

Samy S. El-Tawab Old Dominion University, 2012 Director: Dr. Stephan Olariu The major contribution of this thesis is to lay the theoretical foundations of FRIEND – A cyber-physical system for traffic Flow-Related Information aggrEgatioN and Dissemination. By integrating resources and capabilities at the nexus between the cyber and physical worlds, FRIEND will contribute to aggregating traffic flow data collected by the huge fleet of vehicles on our roads into a comprehensive, near real-time synopsis of traffic flow conditions. We anticipate providing drivers with a meaningful, color-coded, at-a-glance view of flow conditions ahead, alerting them to congested traffic.

FRIEND can be used to provide accurate information about traffic flow and can be used to propagate this information. The workhorse of FRIEND is the ubiquitous lane delimiters (a.k.a. cat’s eyes) on our roadways that, at the moment, are used simply as dumb reflectors.

Our main vision is that by endowing cat’s eyes with a modest power source, detection and communication capabilities they will play an important role in collecting, aggregating and disseminating traffic flow conditions to the driving public. We envision the cat’s eyes system to be supplemented by road-side units (RSU) deployed at regular intervals (e.g. every kilometer or so). The RSUs placed on opposite sides of the roadway constitute a logical unit and are connected by optical fiber under the median. Unlike inductive loop detectors, adjacent RSUs along the roadway are not connected with each other, thus avoiding the huge cost of optical fiber. Each RSU contains a GPS device (for time synchronization), an active Radio Frequency Identification (RFID) tag for communication with passing cars, a radio transceiver for RSU to RSU communication and a laptop-class computing device.

The physical components of FRIEND collect traffic flow-related data from passing vehicles. The collected data is used by FRIEND’s inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic flow-related information along the roadway. The second contribution of this thesis is the development of an incident classification and detection algorithm that can be used to classify different types of traffic incident. Then, it can notify the necessary target of the incident. We also compare our incident detection technique with other VANET techniques.

Our third contribution is a novel strategy for information dissemination on highways. First, we aim to prevent secondary accidents. Second, we notify drivers far away from the accident of an expected delay that gives them the option to continue or exit before reaching the incident location. A new mechanism tracks the source of the incident while notifying drivers away from the accident. The more time the incident stays, the further the information needs to be propagated. Furthermore, the denser the traffic, the faster it will backup. In high density highways, an incident may form a backup of vehicles faster than low density highways. In order to satisfy this point, we need to propagate information as a function of density and time.

iv

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I would like to express my gratitude to Allah (God) for providing me the blessings to complete this work. Also, I could not have completed this thesis without the help of many individuals to whom I would like to express my appreciation. First and foremost, I would like to thank my advisor, Professor Stephan Olariu, who has put a great deal of time and effort into the guidance of this work. Professor Olariu supported me scientifically and guided me as his son. His office was always open to help me solve any technical or personal problem I had during the program. Also, I would like to convey my sincere thanks to Professor Hussein Abdel-wahab for his support and help.

Next, I would like to thank my PhD committee Drs. Michele Weigle, Tamer Nadeem and Dimitrie Popescu. Their expertise, thorough reviewing, continuous support, and valuable suggestions have led to a greatly improved dissertation.

I am also grateful to my family for their encouragement and support. Finally, special thanks to my father and mother for their understanding and patience during the time I spend completing this work.





vi

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48 Different patterns of traffic in case of a blocking and non-blocking incident on highway. Blue lines represent the drivers who have changed lane. Black lines represent the drivers who passed on the pot-hole.......................... 101

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55 Illustrating vehicles color-mapped US13 highway in FRIEND............... 109 56 Illustrating the percentage of message dropped as a function of traffic density.. 112

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1.1 MOTIVATION Several recent US Department of Transportation (US-DOT) and National Highway Traffic Safety Administration (NHTSA) statistics have revealed that in a single year congested highways cost the nation over $70 billion in lost worker productivity and over 8.5 billion gallons of fuel wasted, not to mention high levels of carbon emissions [5, 6]. One important insight offered by these statistics is that over half of all congestion is caused by highway traffic-related incidents rather than by rush-hour traffic in big cities. Congested highways are the leading cause of traffic accidents, and projected data, extrapolated from January-September 2010 statistics (the most recent statistics available at this writing), predict for 2011 an estimated 38,000 traffic-related fatalities [6]. Figure 1 illustrates the principle sources of congestion from a recent US-DOT source [7].

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Unfortunately, on most US highways congestion is a common occurrence and, at the moment, advance notification of imminent congestion is unavailable [7, 8, 5]. It has been This dissertation follows the style of The Physical Review argued convincingly that given sufficient advance notification, drivers could make educated decisions about taking alternate routes; in turn, this would improve traffic safety by reducing the severity of congestion reducing, at the same time, fuel consumption and carbon emissions [9, 10, 11, 12, 13]. In fact, reducing the number of traffic-related accidents, carbon emissions, fuel usage and travel delays on our roadways and city streets has been recognized as one of the National Grand Challenges [14, 15].

Traditionally, traffic monitoring was the purview of various federal and state transportation authorities. In support of providing traffic monitoring and data collection functions a series of methods and procedures, known collectively as Intelligent Transportation Systems (ITS) were set up over the decades. ITS uses mostly legacy technologies such as inductive loop detectors, magnetometers, video detection systems (e.g. cameras), acoustic tracking systems and microwave radar sensors in conjunction with probe vehicles and other means to estimate traffic parameters [16, 17, 18, 14]. The estimated parameters are then aggregated at a central location (usually a Traffic Management Center) and used for various (mostly statistical) purposes. Up to very recently, the collected data and inferred traffic conditions were not shared with the travelling public. It is well documented that the hardware installed in support of collecting traffic-related data is expensive to install and costly to maintain and repair, making hardware-based traffic data collection and incident detection rather ineffective and inefficient [9]. Not surprisingly, the US-DOT has started to investigate a number of possible alternatives [19, 20, 21]. For example, in the next decade, the US-DOT plans to develop an architecture for vehicle infrastructure integration that will collect data from passing vehicles and, after aggregation at a central message switch, will be distributed to the traveling public [22]. The architecture document states that all messages will be digitally signed, with a central certificate authority responsible for distributing public and private encryption keys.

Even though wireless technology was available for the past 90 years, until very recently it was not used to enable communications in support of preventing, or mitigating the effect of, traffic-related events. All this has changed a decade or so ago with the advent of Vehicular Ad-hoc Networks (VANET) that employ a combination of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications intended to give drivers advance notication of traffic-related events. In V2V systems, each vehicle is responsible for inferring the presence of an incident based on reports from other vehicles. This invites a host of well-documented security attacks that could cause vehicles to make incorrect inferences, possibly resulting in increased traffic congestion and a higher chance of severe accidents [23, 24, 25, 26]. In addition, because of their reliance on insecure V2V and V2I communications, most of the VANET systems proposed thus far have serious privacy problems.

Indeed, because V2V communications can be traced back to individual vehicles, the driver of a vehicle will not be able to preserve their privacy and may be subject to impersonation or Sybil attacks. It was recently argued that even if several pseudonyms are used, detecting the true identity of the driver, and the attendant loss of privacy, appears hard to prevent [27, 28, 29, 30].

In support of traffic-related communications, the US Federal Communications Commission (FCC) has allocated 75MHz of spectrum in the 5.850 to 5.925 GHz band specially allocated by the FCC for Dedicated Short Range Communications (DSRC) [31]. It was recently noticed that the DSRC spectrum set aside by the FCC, by far exceeds the needs of traffic-related safety applications [4]. The realization of this fact has already motivated the investigation of offering value-added services including on-line gaming, mobile infotainment, along with various location-specific services [32]. We fully expect third-party infrastructure providers to deploy various forms of road-side infrastructure as well as advanced in-vehicle resources such as embedded powerful computing and storage devices, cognitive radios and cognitive radio networks, and multi-modal programmable sensor nodes. As a result, in the near future, vehicles equipped with computing, communication and sensing capabilities will be organized into ubiquitous and pervasive networks with virtually unlimited Internet access while on the move. This will revolutionize the driving experience making it safer, more enjoyable, and more environmentally friendly.

In spite of the phenomenal advances in wireless technology, it was soon recognized that V2V and V2I communications, by themselves, do not suffice to prevent congestion and/or to mitigate its effect. The missing link is a tight integration, at several scales, of the capabilities of VANET and ITS. Not surprisingly, the past few years have seen a rapid converge of VANET and ITS leading to the emergence of Intelligent Vehicular Networks (InVeNet) with the expectation to revolutionize the way we drive by creating a safe, secure, and robust ubiquitous computing environment that will eventually pervade our highways and city streets. Lately, various solutions for traffic monitoring and incident detection have been proposed at the nexus of VANET and ITS.

However, the synergy of a confluence between VANET and ITS does not, in and by itself, address the most critical issues that underlie the deployment of automated incident detection and traffic-related information dissemination to the driving public. For example, the systems proposed thus far are neither secure nor privacy-aware, leaving the drivers vulnerable to location tracking, impersonation or Sybil attacks. The problem stems from the fact that, even with the use of pseudonyms, vehicular communications can be traced back to individual cars. As VANET is being integrated into the fabric of the society, secure networking is fundamental to achieve trustworthiness and effective operation in such a decentralized environment consisting of thousands of autonomous nodes with heterogeneous capabilities. We do not yet have a well-validated and widely-accepted set of design principles for building such systems.

Thus, there is a need for a secure and privacy-aware system that automatically detects existing traffic conditions and anticipates discernible trends in the traffic flow, based on which it can intelligently predict imminent traffic events and alert the driving public to their likely occurrence. Such a system, commonly referred to as a cyber-physical system (CPS), must integrate in a coherent way and at various scales the resources and capabilities of its hardware and the software components. A CPS can be thought of as a perfect example of the classical adage asserting that “The whole is more than the sum of its parts”.



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