Optimization of adaptive traffic signal control with transit signal priority at isolated intersections using parallel genetic algorithms

Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This dissertation presents a new method that applies the parallel genetic algorithm (PGA) to optimize adaptive traffic signal control in the presence of transit signal priority (TSP). The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. Unlike the simple genetic algorithm (GA), PGA can provide better and faster solutions needed for real-time optimization of adaptive traffic signal control. ^ An important component in the proposed method involves the development of a microscopic delay estimation model that was designed specifically to optimize adaptive traffic signal with TSP. Macroscopic delay models such as the Highway Capacity Manual (HCM) delay model are unable to accurately consider the effect of phase combination and phase sequence in delay calculations. In addition, because the number of phases and the phase sequence of adaptive traffic signal may vary from cycle to cycle, the phase splits cannot be optimized when the phase sequence is also a decision variable. A "flex-phase" concept was introduced in the proposed microscopic delay estimation model to overcome these limitations. ^ The performance of PGA was first evaluated against the simple GA. The results show that PGA achieved both faster convergence and lower delay for both under- or over-saturated traffic conditions. A VISSIM simulation testbed was then developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer was able to produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles. The VISSIM testbed developed in this research provides a powerful tool to design and evaluate different TSP strategies under both actuated and adaptive signal control. ^

Traffic congestion control in communication networks

Next generation networks are characterized by ever increasing complexity, intelligence, heterogeneous technologies and increasing user expectations. Telecommunication networks in particular have become truly global, consisting of a variety of national and regional networks, both wired and wireless. Consequently, the management of telecommunication networks is becoming increasingly complex. In addition, network security and reliability requirements require additional overheads which increase the size of the data records. This in turn causes acute network traffic congestions. There is no single network management methodology to control the various requirements of today's networks, and provides a good level of Quality of Service (QoS), and network security. Therefore, an integrated approach is needed in which a combination of methodologies can provide solutions and answers to network events (which cause severe congestions and compromise the quality of service and security). The proposed solution focused on a systematic approach to design a network management system based upon the recent advances in the mobile agent technologies. This solution has provided a new traffic management system for telecommunication networks that is capable of (1) reducing the network traffic load (thus reducing traffic congestion), (2) overcoming existing network latency, (3) adapting dynamically to the traffic load of the system, (4) operating in heterogeneous environments with improved security, and (5) having robust and fault tolerance behavior. This solution has solved several key challenges in the development of network management for telecommunication networks using mobile agents. We have designed several types of agents, whose interactions will allow performing some complex management actions, and integrating them. Our solution is decentralized to eliminate excessive bandwidth usage and at the same time has extended the capabilities of the Simple Network Management Protocol (SNMP). Our solution is fully compatible with the existing standards.

Continuous property measurement techniques and physics based mathematical model for frost growth control

The development of a new set of frost property measurement techniques to be used in the control of frost growth and defrosting processes in refrigeration systems was investigated. Holographic interferometry and infrared thermometry were used to measure the temperature of the frost-air interface, while a beam element load sensor was used to obtain the weight of a deposited frost layer. The proposed measurement techniques were tested for the cases of natural and forced convection, and the characteristic charts were obtained for a set of operational conditions. ^ An improvement of existing frost growth mathematical models was also investigated. The early stage of frost nucleation was commonly not considered in these models and instead an initial value of layer thickness and porosity was regularly assumed. A nucleation model to obtain the droplet diameter and surface porosity at the end of the early frosting period was developed. The drop-wise early condensation in a cold flat plate under natural convection to a hot (room temperature) and humid air was modeled. A nucleation rate was found, and the relation of heat to mass transfer (Lewis number) was obtained. It was found that the Lewis number was much smaller than unity, which is the standard value usually assumed for most frosting numerical models. The nucleation model was validated against available experimental data for the early nucleation and full growth stages of the frosting process. ^ The combination of frost top temperature and weight variation signals can now be used to control the defrosting timing and the developed early nucleation model can now be used to simulate the entire process of frost growth in any surface material. ^

Soot measurements in steady and pulsed ethylene/air diffusion flames using Laser-Induced Incandescence

Combustion-generated carbon black nano particles, or soot, have both positive and negative effects depending on the application. From a positive point of view, it is used as a reinforcing agent in tires, black pigment in inks, and surface coatings. From a negative point of view, it affects performance and durability of many combustion systems, it is a major contributor of global warming, and it is linked to respiratory illness and cancer. Laser-Induced Incandescence (LII) was used in this study to measure soot volume fractions in four steady and twenty-eight pulsed ethylene diffusion flames burning at atmospheric pressure. A laminar coflow diffusion burner combined with a very-high-speed solenoid valve and control circuit provided unsteady flows by forcing the fuel flow with frequencies between 10 Hz and 200 Hz. Periodic flame oscillations were captured by two-dimensional phase-locked LII images and broadband luminosity images for eight phases (0° – 360°) covering each period. A comparison between the steady and pulsed flames and the effect of the pulsation frequency on soot volume fraction in the flame region and the post flame region are presented. The most significant effect of pulsing frequency was observed at 10 Hz. At this frequency, the flame with the lowest mean flow rate had 1.77 times enhancement in peak soot volume fraction and 1.2 times enhancement in total soot volume fraction; whereas the flame with the highest mean flow rate had no significant change in the peak soot volume fraction and 1.4 times reduction in the total soot volume fraction. A correlation (fvRe-1 = a + b·Str) for the total soot volume fraction in the flame region for the unsteady laminar ethylene flames was obtained for the pulsation frequency between 10 Hz and 200 Hz, and the Reynolds number between 37 and 55. The soot primary particle size in steady and unsteady flames was measured using the Time-Resolved Laser-Induced Incandescence (TIRE-LII) and the double-exponential fit method. At maximum frequency (200 Hz), the soot particles were smaller in size by 15% compared to the steady case in the flame with the highest mean flow rate.

Cooperative and adaptive medium access control in multi-hop wireless networks

Due to low cost and easy deployment, multi-hop wireless networks become a very attractive communication paradigm. However, IEEE 802.11 medium access control (MAC) protocol widely used in wireless LANs was not designed for multi-hop wireless networks. Although it can support some kinds of ad hoc network architecture, it does not function efficiently in those wireless networks with multi-hop connectivity. Therefore, our research is focused on studying the medium access control in multi-hop wireless networks. The objective is to design practical MAC layer protocols for supporting multihop wireless networks. Particularly, we try to prolong the network lifetime without degrading performances with small battery-powered devices and improve the system throughput with poor quality channels. ^ In this dissertation, we design two MAC protocols. The first one is aimed at minimizing energy-consumption without deteriorating communication activities, which provides energy efficiency, latency guarantee, adaptability and scalability in one type of multi-hop wireless networks (i.e. wireless sensor network). Methodologically, inspired by the phase transition phenomena in distributed networks, we define the wake-up probability, which maintained by each node. By using this probability, we can control the number of wireless connectivity within a local area. More specifically, we can adaptively adjust the wake-up probability based on the local network conditions to reduce energy consumption without increasing transmission latency. The second one is a cooperative MAC layer protocol for multi-hop wireless networks, which leverages multi-rate capability by cooperative transmission among multiple neighboring nodes. Moreover, for bidirectional traffic, the network throughput can be further increased by using the network coding technique. It is a very helpful complement for current rate-adaptive MAC protocols under the poor channel conditions of direct link. Finally, we give an analytical model to analyze impacts of cooperative node on the system throughput. ^

Incorporating variable peak -to -daily ratios in the equilibrium traffic assignment procedure of the Florida Standard Urban Transportation Modeling Structure (FSUTMS)

The standard highway assignment model in the Florida Standard Urban Transportation Modeling Structure (FSUTMS) is based on the equilibrium traffic assignment method. This method involves running several iterations of all-or-nothing capacity-restraint assignment with an adjustment of travel time to reflect delays encountered in the associated iteration. The iterative link time adjustment process is accomplished through the Bureau of Public Roads (BPR) volume-delay equation. Since FSUTMS' traffic assignment procedure outputs daily volumes, and the input capacities are given in hourly volumes, it is necessary to convert the hourly capacities to their daily equivalents when computing the volume-to-capacity ratios used in the BPR function. The conversion is accomplished by dividing the hourly capacity by a factor called the peak-to-daily ratio, or referred to as CONFAC in FSUTMS. The ratio is computed as the highest hourly volume of a day divided by the corresponding total daily volume. ^ While several studies have indicated that CONFAC is a decreasing function of the level of congestion, a constant value is used for each facility type in the current version of FSUTMS. This ignores the different congestion level associated with each roadway and is believed to be one of the culprits of traffic assignment errors. Traffic counts data from across the state of Florida were used to calibrate CONFACs as a function of a congestion measure using the weighted least squares method. The calibrated functions were then implemented in FSUTMS through a procedure that takes advantage of the iterative nature of FSUTMS' equilibrium assignment method. ^ The assignment results based on constant and variable CONFACs were then compared against the ground counts for three selected networks. It was found that the accuracy from the two assignments was not significantly different, that the hypothesized improvement in assignment results from the variable CONFAC model was not empirically evident. It was recognized that many other factors beyond the scope and control of this study could contribute to this finding. It was recommended that further studies focus on the use of the variable CONFAC model with recalibrated parameters for the BPR function and/or with other forms of volume-delay functions. ^

Background traffic modeling for large-scale network simulation

Network simulation is an indispensable tool for studying Internet-scale networks due to the heterogeneous structure, immense size and changing properties. It is crucial for network simulators to generate representative traffic, which is necessary for effectively evaluating next-generation network protocols and applications. With network simulation, we can make a distinction between foreground traffic, which is generated by the target applications the researchers intend to study and therefore must be simulated with high fidelity, and background traffic, which represents the network traffic that is generated by other applications and does not require significant accuracy. The background traffic has a significant impact on the foreground traffic, since it competes with the foreground traffic for network resources and therefore can drastically affect the behavior of the applications that produce the foreground traffic. This dissertation aims to provide a solution to meaningfully generate background traffic in three aspects. First is realism. Realistic traffic characterization plays an important role in determining the correct outcome of the simulation studies. This work starts from enhancing an existing fluid background traffic model by removing its two unrealistic assumptions. The improved model can correctly reflect the network conditions in the reverse direction of the data traffic and can reproduce the traffic burstiness observed from measurements. Second is scalability. The trade-off between accuracy and scalability is a constant theme in background traffic modeling. This work presents a fast rate-based TCP (RTCP) traffic model, which originally used analytical models to represent TCP congestion control behavior. This model outperforms other existing traffic models in that it can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding packet-oriented simulation. Third is network-wide traffic generation. Regardless of how detailed or scalable the models are, they mainly focus on how to generate traffic on one single link, which cannot be extended easily to studies of more complicated network scenarios. This work presents a cluster-based spatio-temporal background traffic generation model that considers spatial and temporal traffic characteristics as well as their correlations. The resulting model can be used effectively for the evaluation work in network studies.

A Static Traffic Assignment Model Combined with an Artificial Neural Network Delay Model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

Soot Measurements in Steady and Pulsed Ethylene/Air Diffusion Flames Using Laser-Induced Incandescence

Combustion-generated carbon black nano particles, or soot, have both positive and negative effects depending on the application. From a positive point of view, it is used as a reinforcing agent in tires, black pigment in inks, and surface coatings. From a negative point of view, it affects performance and durability of many combustion systems, it is a major contributor of global warming, and it is linked to respiratory illness and cancer. Laser-Induced Incandescence (LII) was used in this study to measure soot volume fractions in four steady and twenty-eight pulsed ethylene diffusion flames burning at atmospheric pressure. A laminar coflow diffusion burner combined with a very-high-speed solenoid valve and control circuit provided unsteady flows by forcing the fuel flow with frequencies between 10 Hz and 200 Hz. Periodic flame oscillations were captured by two-dimensional phase-locked LII images and broadband luminosity images for eight phases (0°- 360°) covering each period. A comparison between the steady and pulsed flames and the effect of the pulsation frequency on soot volume fraction in the flame region and the post flame region are presented. The most significant effect of pulsing frequency was observed at 10 Hz. At this frequency, the flame with the lowest mean flow rate had 1.77 times enhancement in peak soot volume fraction and 1.2 times enhancement in total soot volume fraction; whereas the flame with the highest mean flow rate had no significant change in the peak soot volume fraction and 1.4 times reduction in the total soot volume fraction. A correlation (ƒv Reˉ1 = a+b· Str) for the total soot volume fraction in the flame region for the unsteady laminar ethylene flames was obtained for the pulsation frequency between 10 Hz and 200 Hz, and the Reynolds number between 37 and 55. The soot primary particle size in steady and unsteady flames was measured using the Time-Resolved Laser-Induced Incandescence (TIRE-LII) and the double-exponential fit method. At maximum frequency (200 Hz), the soot particles were smaller in size by 15% compared to the steady case in the flame with the highest mean flow rate.

Background Traffic Modeling for Large-Scale Network Simulation

Network simulation is an indispensable tool for studying Internet-scale networks due to the heterogeneous structure, immense size and changing properties. It is crucial for network simulators to generate representative traffic, which is necessary for effectively evaluating next-generation network protocols and applications. With network simulation, we can make a distinction between foreground traffic, which is generated by the target applications the researchers intend to study and therefore must be simulated with high fidelity, and background traffic, which represents the network traffic that is generated by other applications and does not require significant accuracy. The background traffic has a significant impact on the foreground traffic, since it competes with the foreground traffic for network resources and therefore can drastically affect the behavior of the applications that produce the foreground traffic. This dissertation aims to provide a solution to meaningfully generate background traffic in three aspects. First is realism. Realistic traffic characterization plays an important role in determining the correct outcome of the simulation studies. This work starts from enhancing an existing fluid background traffic model by removing its two unrealistic assumptions. The improved model can correctly reflect the network conditions in the reverse direction of the data traffic and can reproduce the traffic burstiness observed from measurements. Second is scalability. The trade-off between accuracy and scalability is a constant theme in background traffic modeling. This work presents a fast rate-based TCP (RTCP) traffic model, which originally used analytical models to represent TCP congestion control behavior. This model outperforms other existing traffic models in that it can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding packet-oriented simulation. Third is network-wide traffic generation. Regardless of how detailed or scalable the models are, they mainly focus on how to generate traffic on one single link, which cannot be extended easily to studies of more complicated network scenarios. This work presents a cluster-based spatio-temporal background traffic generation model that considers spatial and temporal traffic characteristics as well as their correlations. The resulting model can be used effectively for the evaluation work in network studies.