Freight transportation is a significant component of the nation’s economy. However, the augmented volume of the freight movements contributed to continuously increasing congestion on the urban road networks, that affects the timeliness and reliability of freight transportation. In addition, congestion has a negative impact on the transit operations as well. Various studies conducted on multi-modal corridors recognized the importance of the simultaneous performance of freight and transit operations. Thus, Intelligent Transportation System (ITS) components, such as Freight Signal Priority (FSP) and Transit Signal Priority (TSP), present traffic operations strategies "shaped" to give priority, reduce delay and travel time, and overall improve the performance of freight and transit movements, respectively. The primary objective of the thesis refers to evaluate possible improvements in freight mobility, while sustaining good transit services and minimizing congestion on the multi-modal corridor, through simultaneous implementation of the FSP and the TSP. The effectiveness of the newly established criteria was evaluated through real-world case study on a micro-simulation platform. The results showed significant improvements on all the vehicle movements. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Thesis (MScIng)--University of Stellenbosch, 2007. / Bibliography. Also available via the Internet.

Thesis (Ph. D.)--University of Louisville, 2008. / Department of Civil and Environmental Engineering. Vita. "December 2008." Includes bibliographical references (leaves 193-197).

Dissertation (PhD)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

The traffic safety and efficiency applications made possible by vehicular communications have the potential to improve the lives of millions of people who, every day, use automobiles as their primary means of transportation. To be well connected and fully functional, these networks of cars require a minimum number of active nodes, which often may not happen due to a lack of radio-equipped vehicles on the road. These same networks can also be overwhelmed with traffic and signaling in the presence of too many cars, requiring careful coordination between all nodes to ensure proper operation. One way to overcome both these problems is to supplement vehicle-to-vehicle (V2V) communications with vehicle-to-infrastructure (V2I) systems by deploying Roadside Units (RSUs) along the road to support the network of moving cars. RSUs are infrastructure nodes that can supplement sparse networks in low-density scenarios, and help coordinate and move data in denser networks. RSUs have an associated cost, however, and so their numbers need to be minimized while still maintaining a significant improvement to the vehicular network. he work presented in this thesis quantities the benefits of Roadside Unit deployments and proposes innovative approaches that can reduce and even eliminate the need for RSUs altogether.he first part of the thesis focuses on highway networks: first, an analytical model is developed to analyze communication delay in scenarios with sparse bi-directional traffic, considering both disconnected and connected RSUs.hen, a study on connectivity and message dissemination in these networks reveals how significant benefits of RSUs are only achieved when the deployed RSUs are interconnected. Extensive simulation work paired with sets of experimental measurements validate both model and study. Supplementing the work on sparse highway networks, an infrastructure-less approach is then proposed, consisting of two methods to improve communication delays in these scenarios: decelerate disconnected vehicles as they receive safety messages, and boost the same vehicles’ radio transmit power, to shorten the time to restore connectivity. Both techniques are modeled analytically, and data from a simulation study validate the models and show significant improvements in the connectivity of sparse highway networks with this infrastructure-less approach. he second part of the thesis sets its sights on urban vehicular networks. High costs associated with RSUs prevent their deployment at scale, and therefore finding alternative solutions to this longstanding problem is very important. A novel, low-cost self-organizing network approach to leveraging parked cars as RSUs in urban areas is proposed here, enabling parked cars to create coverage maps based on received signal strength and to decide whether to become RSUs from that knowledge. Initial simulation work reveals significant benefits to emergency message broadcasting delay in sparse scenarios and shows the ability of the self-organizing approach in providing robust and widespread coverage to dense urban areas, using only a small fraction of the cars parked in a city. he parking behaviors of individual drivers are then studied, by analyzing and gathering statistics on travel survey data from various metropolitan areas. Daily and hourly analytical models of parking events are provided, along with important derivations.he statistical data show that parking events can be classified into two major groups based on the time a car spends parked, and that these patterns vary substantially throughout the day while being markedly similar across different cities. he last part of the thesis focuses on self-organization for parked car RSUs. Novel mechanisms for self-organization are introduced that are innovative in their ability to keep the network of parked cars under continuous optimization, in their multicriteria decision process, and in their control of each car’s battery usage, rotating roadside unit roles between vehicles as required.he first comprehensive study of the performance of such approaches is presented, via realistic modeling of mobility, parking, and communication, thorough simulations, and an experimental verification of concepts that are key to self-organization. his analysis leads to strong evidence that parked cars can serve as an alternative to fixed roadside units, and organize to form networks to support smarter transportation and mobility.

Traffic congestion and over saturation is a common worldwide problem. This problem is more severe in urban areas due to rapid increase in number of vehicles. City traffic often results in traffic blockage at peak office hours. The same situation can also be observed on freeways, especially at entrance/exit areas of a city. In most cases the drivers are totally unaware about road traffic situations in advance. Intelligent transportation systems offer various applications to improve road traffic.  In this manner the timely and reliable traffic information can be greatly beneficial for travelers. Real time travel time information can be adopted by vehicles as an excellent tool which can result to reduce the impact of continuous traffic increase on urban roads. Real time travel time estimation is a complex task as it involves various factors. This travel time estimation is even more difficult for urban road networks. In our dissertation, we have investigated an estimated time of arrival approach in order to inform the drivers in advance about travel time. We have introduced Estimated Time of Arrival (ETA) in a clever and intelligent fashion by developing a Case-Based Reasoning engine. We believe that by adopting this simple and intelligent approach, it can greatly help improving traffic congestion overall. We also believe that this strategy will also help in reducing the emission of environment unfriendly gases, thus helping mankind.

Indiana University-Purdue University Indianapolis (IUPUI) / Gholamjafari, Ali MSECE, Purdue University, May 2015. A Genetic Algorithm Approach to Best Scenarios Selection for Performance Evaluation of Vehicle Active Safety Systems . Major Professor: Dr. Lingxi Li. One of the most crucial tasks for Intelligent Transportation Systems is to enhance driving safety. During the past several years, active safety systems have been broadly studied and they have been playing a significant role in vehicular safety. Pedestrian Pre- Collision System (PCS) is a type of active safety systems which is used toward pedestrian safety. Such system utilizes camera, radar or a combination of both to detect the relative position of the pedestrians towards the vehicle. Based on the speed and direction of the car, position of the pedestrian, and other useful information, the systems can anticipate the collision/near-collision events and take proper actions to reduce the damage due to the potential accidents. The actions could be triggering the braking system to stop the car automatically or could be simply sending a warning signal to the driver depending on the type of the events. We need to design proper testing scenarios, perform the vehicle testing, collect and analyze data to evaluate the performance of PCS systems. It is impossible though to test all possible accident scenarios due to the high cost of the experiments and the time limit. Therefore, a subset of complete testing scenarios (which is critical due to the different types of cost such as fatalities, social costs, the numbers of crashes, etc.) need to be considered instead. Note that selecting a subset of testing scenarios is equivalent to an optimization problem which is maximizing a cost function while satisfying a set of constraints. In this thesis, we develop an approach based on Genetic Algorithm to solve such optimization problems. We then utilize crash and field database to validate the accuracy of our algorithm. We show that our method is effective and robust, and runs much faster than exhaustive search algorithms. We also present some crucial testing scenarios as the result of our approach, which can be used in PCS field testing.

Intelligent Transportation Systems (ITS) have emerged to use different technologies to promote safety, convenience, and efficiency of transportation networks. Many applications of ITS depend on the availability of the real-time positioning of the vehicles in the network. In this research, the two open challenges in the field of vehicle localization for ITS are introduced and addressed. First, in order to have safe and efficient transportation systems, the locations of the vehicles need to be available everywhere in a network. Conventional localization techniques mostly rely on Global Positioning System (GPS) technology which cannot meet the accuracy requirements for all applications in all situations. This work advances the study of vehicle positioning in ITS by introducing an integrated positioning framework which uses several resources including GPS, vehicle-to-infrastructure and vehicle-to-vehicle communications, radio-frequency identification, and dead reckoning. These technologies are used to provide more reliable and accurate location information. The suggested framework fills the gap between the accuracy of the current vehicle localization techniques and the required one for many ITS applications. Second, different ITS applications have different localization accuracy and latency requirements. A smart positioning algorithm is proposed which enable us to change the positioning accuracy delivered by the algorithm based on different applications. The algorithm utilizes only the most effective resources to achieve the required accuracy, even if more resources are available. In this way, the complexity of the system and the running time decrease while the desired accuracy is obtained. The adjective Smart is selected because the algorithm smartly selects the most effective connection which has the most contribution to vehicle positioning when a connection needs to be added. On the other hand, when a connection should be removed, the algorithm smartly selects the least effective one which has the least contribution to the position estimation. This study also provides an overview about the positioning requirements for different ITS applications. A close-to-real-world scenario has been developed and simulated in MATLAB to evaluate the performance of the proposed algorithms. The simulation results show that the vehicle can acquire accurate location in different environments using the suggested Integrated framework. Moreover, the advantages of the proposed Smart algorithm in terms of accuracy and running time are presented through a series of comprehensive simulations. / Master of Science

published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning