It is widely agreed that estimates of freeway segment travel times are more highly valued by motorists than other forms of traveller information. The provision of real-time estimates of travel times is becoming relatively common in many of the large urban centres in the US and overseas. Presently, most traveler information systems are operating based on estimated travel time rather than predicted travel time. However, traveler information systems are most beneficial when they are built upon predicted traffic information (e.g. predicted travel time). A number of researchers have proposed different models to predict travel time. One of these techniques is based on traffic flow theory and the concept of shockwaves. Most of the past efforts at identifying shockwaves have been focused on performing shockwave analysis based on fixed sensors such as loop detectors which are commonly used in many jurisdictions. However, latest advances in wireless communications have provided an opportunity to obtain vehicle trajectory data that potentially could be used to derive traffic conditions over a wide spatial area. This research proposes a new methodology to detect and analyze shockwaves based on vehicle trajectory data and will use this information to predict travel time for freeway sections. The main idea behind this methodology is that average speed on a section of roadway is constant unless a shockwave is created due to change in flow rate or density of traffic. In the proposed methodology first the road section is discretized into a number of smaller road segments and the average speed of each segment is calculated based on the available information obtained from probe vehicles during the current time interval. If a new shockwave is detected, the average speed of the road segment is adjusted to account for the change in the traffic conditions. In order to detect shockwaves, first, a two phase piecewise linear regression is used to find the points at which a vehicle has changed its speed. Then, the points that correspond to the intersection of shockwaves and trajectories of probe vehicles are identified using a data filtering procedure and a linear clustering algorithm is employed to group different shockwaves. Finally, a linear regression model is applied to find propagation speed and spatial and temporal extent of each shockwave. The performance of this methodology was tested using one simulated signalized intersection, trajectories obtained from video processing of a section of freeway in California, and trajectories obtained from two freeway sections in Ontario. The results of this thesis show that the proposed methodology is able to detect shockwaves and predict travel time even with a small sample of vehicles. These results show that traffic data acquisition systems which are based on anonymously tracking of vehicles are a viable substitution to the tradition traffic data collection systems especially in relatively rural areas.

It is widely agreed that estimates of freeway segment travel times are more highly valued by motorists than other forms of traveller information. The provision of real-time estimates of travel times is becoming relatively common in many of the large urban centres in the US and overseas. Presently, most traveler information systems are operating based on estimated travel time rather than predicted travel time. However, traveler information systems are most beneficial when they are built upon predicted traffic information (e.g. predicted travel time). A number of researchers have proposed different models to predict travel time. One of these techniques is based on traffic flow theory and the concept of shockwaves. Most of the past efforts at identifying shockwaves have been focused on performing shockwave analysis based on fixed sensors such as loop detectors which are commonly used in many jurisdictions. However, latest advances in wireless communications have provided an opportunity to obtain vehicle trajectory data that potentially could be used to derive traffic conditions over a wide spatial area. This research proposes a new methodology to detect and analyze shockwaves based on vehicle trajectory data and will use this information to predict travel time for freeway sections. The main idea behind this methodology is that average speed on a section of roadway is constant unless a shockwave is created due to change in flow rate or density of traffic. In the proposed methodology first the road section is discretized into a number of smaller road segments and the average speed of each segment is calculated based on the available information obtained from probe vehicles during the current time interval. If a new shockwave is detected, the average speed of the road segment is adjusted to account for the change in the traffic conditions. In order to detect shockwaves, first, a two phase piecewise linear regression is used to find the points at which a vehicle has changed its speed. Then, the points that correspond to the intersection of shockwaves and trajectories of probe vehicles are identified using a data filtering procedure and a linear clustering algorithm is employed to group different shockwaves. Finally, a linear regression model is applied to find propagation speed and spatial and temporal extent of each shockwave. The performance of this methodology was tested using one simulated signalized intersection, trajectories obtained from video processing of a section of freeway in California, and trajectories obtained from two freeway sections in Ontario. The results of this thesis show that the proposed methodology is able to detect shockwaves and predict travel time even with a small sample of vehicles. These results show that traffic data acquisition systems which are based on anonymously tracking of vehicles are a viable substitution to the tradition traffic data collection systems especially in relatively rural areas.

Firmly placed in an industrial setting, this research explored the introduction of remote monitoring technology into three different organizational contexts. By following these organizations over time, starting with their intention to invest in remote monitoring systems (RMS), there was a unique opportunity to witness their processes and to gain an insight into the intricacies of information technology (IT) and organizational transformation. The main question that this research sought to answer was thus: How is IT implicated in the remote monitoring of industrial equipment? Previous information systems research has been accused of not paying enough attention to the material, that is, of not being specific about technology. This research adopted a sociomaterial perspective, thus recognizing the constitutive entanglement of the material and the social, and thereby acknowledging their mutual dependency. However, in order to provide specific insights about the material, an analytical disentanglement was performed, by extending the concept of agency from a focus on “the what” to include “the how”. Change was thus studied by not only asking what the nature of change is and who or what causes change to occur, but by also tracing how change is enacted, thus capturing both material and social agency as well as tracing their entanglement. This research employed a broad approach, designed to provide a profound and extensive account of the studied phenomenon. Consequently, the thesis explored value creation, sourcing routines, partnership formation and innovative practices all related to remote diagnostics design and use. In addition, the research was qualitative and used interpretive case studies as the main methodology. A composite finding of this research is that an RMS, with its ability to collect, transmit, store, and analyze specific contextual information across time and space, provides opportunities for boundary-spanning activities manifested as the formation of interorganizational networks. Furthermore, by tracing the information capabilities of the IT, and by being specific about the material, it has been possible to explore how RMSs have the potential to influence both organizational form and content. Through the examination of RMSs within interorganizational networks and as a part of value creation practices, this research has also shown how the organizational form and content have the potential to influence RMSs; their design, use, and material affordances. This research also placed focus on the importance of trust and has shown that trust in technology is established through trust in people.

Τα τελευταία χρόνια υπάρχει μια αυξημένη χρήση των κλειστών συστημάτων παρακολούθησης, είτε αυτά αφορούν την επιτήρηση κλειστών χώρων για λόγους ασφάλειας, είτε για παρακολούθηση της κυκλοφορίας σε δρόμους αυξημένης κυκλοφορίας ή ακόμα και για στατιστικούς λόγους για τον υπολογισμό επισκεπτών σε εμπορικά κέντρα ή διερχόμενων οχημάτων ή επιβατών σε μέσα μαζικής μεταφοράς, περίπτωση την οποία εξετάζουμε στην παρούσα εργασία. Η μεθοδολογία που αναπτύχτηκε περιλαμβάνει τρία κυρίως στάδια, την αφαίρεση του στατικού φόντου, την ανίχνευση των κινούμενων ατόμων και τον εντοπισμό τους σε όλη διαδρομή τους. / In recent years there has been an increased use of monitoring systems, whether they concern the monitoring of closed areas for security reasons or to monitor traffic on roads with increased traffic or even for statistical purposes to calculate visitors to shopping centers or transit vehicles or passenger to public transportation, which we are examining in this thesis. The methodology was developed in three main stages, removing the static background, the detection of moving people and track them throughout their journey.

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