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

All passengers travel at the hour most convenient to them. But it is not always possible to find a flight at the right time to fly them to their destination. In the case where service in any one time period is insufficient to meet air travel demanded, it may be expected that some unfilled demand passengers will either delay their flight or will advance it, thus adding to the effective demand of the adjoining time periods.The obvious alternate means of travel is a rental car. It takes a lot more time than flight, but it is readily available at any given time. This brings us to think of an airline system that will work in a similar fashion; A system that can be named an "Air Taxi System." This would mean a virtual highway in air space leading to a vast network. The network would be served by small aircraft flying from one city to another loading and unloading passengers. Such a large network having dynamic demand will have many issues to resolve before successfully launching a Small Aircraft Transportation System. One of the most important problems to solve is scheduling of aircraft for such a stochastic demand flow. The objective of the research is to study a given set of airports with dynamic demand and known aircraft type. The major task will be to analyze the flow of passengers between each origin-destination pair and then schedule flights. The research will be to develop a schedule for a fixed set of airports with dynamic demand and known type of aircraft. The main objective is to maximize demand satisfaction. The study will also analyze the number of aircraft required for a given set of airports and find a method to schedule them. / 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

Artificial intelligence research is ushering in an era of sophisticated, mass-market transportation technology. While computers can fly a passenger jet better than a human pilot, people still face the dangerous yet tedious task of driving. Intelligent Transportation Systems (ITS) is the field focused on integrating information technology with vehicles and transportation infrastructure. Recent advances in ITS point to a future in which vehicles handle the vast majority of the driving task. Once autonomous vehicles become popular, interactions amongst multiple vehicles will be possible. Current methods of vehicle coordination will be outdated. The bottleneck for efficiency will no longer be drivers, but the mechanism by which those drivers' actions are coordinated. Current methods for controlling traffic cannot exploit the superior capabilities of autonomous vehicles. This thesis describes a novel approach to managing autonomous vehicles at intersections that decreases the amount of time vehicles spend waiting. Drivers and intersections in this mechanism are treated as autonomous agents in a multiagent system. In this system, agents use a new approach built around a detailed communication protocol, which is also a contribution of the thesis. In simulation, I demonstrate that this mechanism can significantly outperform current intersection control technology-traffic signals and stop signs. This thesis makes several contributions beyond the mechanism and protocol. First, it contains a distributed, peer-to-peer version of the protocol for low-traffic intersections. Without any requirement of specialized infrastructure at the intersection, such a system would be inexpensive and easy to deploy at intersections which do not currently require a traffic signal. Second, it presents an analysis of the mechanism's safety, including ways to mitigate some failure modes. Third, it describes a custom simulator, written for this work, which will be made publicly available following the publication of the thesis. Fourth, it explains how the mechanism is "backward-compatible" so that human drivers can use it alongside autonomous vehicles. Fifth, it explores the implications of using the mechanism at multiple proximal intersections. The mechanism, along with all available modes of operation, is implemented and tested in simulation, and I present experimental results that strongly attest to the efficacy of this approach. / text

This study attempts to develop realistic and relevant investment planning models for inland container transportation systems. An inland container transport system model has been constructed consisting of three sub-models: forecasting future total export container demand, the inland container traffic allocation model and the optimum port capacity model. The models may be utilised to identify the most effective investment plan for inland transportation infrastructure development and to evaluate the inland container transportation system. The procedure enables determination of the optimal locations, sizes and time of container port developments as well as the optimal container cargo flows through transportation networks. A Heuristic algorithm was developed for the purpose of evaluating alternative investment plans. Dynamic and Linear programming methods are applied to each of the two planning problems: the former for the optimum container port capacity development problem and the latter for the optimal allocation of inland container traffic movements. Finally, the model has been applied to concrete inland container transportation system problems in Korea. The results are reported and analysed. It is hoped that they may provide a guideline for actual development.

Lau Wai Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 63-65). / Abstracts in English and Chinese. / abstract --- p.I / 摘要 --- p.II / acknowledgement --- p.III / table of contents --- p.IV / list of figures --- p.VI / Chapter CHAPTER 1 --- introduction --- p.1 / Chapter CHAPTER 2 --- literature review --- p.4 / Chapter 2.1 --- Traffic data collection methods --- p.4 / Chapter 2.2 --- Vision-based traffic monitoring techniques --- p.6 / Chapter 2.2.1 --- Vehicle tracking approaches --- p.7 / Chapter 2.2.2 --- Image processing techniques --- p.10 / Chapter CHAPTER 3 --- methodology --- p.15 / Chapter 3.1 --- Solution Concept --- p.16 / Chapter 3.2 --- System Framework --- p.18 / Chapter 3.2.1 --- Edge Detection Module --- p.20 / Chapter 3.2.2 --- Background Update Module --- p.22 / Chapter 3.2.3 --- Feature Extraction Modules --- p.25 / Chapter CHAPTER 4 --- experiments and evaluation --- p.41 / Chapter 4.1 --- Setup and Data Collection --- p.41 / Chapter 4.2 --- Evaluation Criteria --- p.42 / Chapter 4.3 --- Experimental Results --- p.44 / Chapter 4.3.1 --- Comparing overall accuracies --- p.44 / Chapter 4.3.2 --- Accuracies for different traffic conditions --- p.46 / Chapter 4.3.3 --- Comparing balanced sampling and random sampling --- p.48 / Chapter 4.3.4 --- Comparing day and night conditions --- p.50 / Chapter 4.3.5 --- Testing on time-series of images --- p.52 / Chapter CHAPTER 5 --- analysis --- p.54 / Chapter 5.1 --- Strengths and Weaknesses --- p.54 / Chapter 5.1.1 --- Sobel Edge Histogram --- p.54 / Chapter 5.1.2 --- Horizontal Line Detection --- p.55 / Chapter 5.1.3 --- Block Detection --- p.56 / Chapter 5.1.4 --- Combined Learning --- p.57 / Chapter 5.1.5 --- Overall Framework --- p.58 / Chapter 5.2 --- Future Research --- p.59 / Chapter 5.2.1 --- Static image based monitoring combined with other traffic monitoring approaches --- p.59 / Chapter 5.2.2 --- Horizontal Line Detection as tracked features of vehicles --- p.60 / Chapter 5.2.3 --- Application in aerial image-based system --- p.60 / Chapter CHAPTER 6 --- conclusion --- p.62 / bibliography --- p.63 / appendix a sobel edge detection --- p.66 / appendix b neural network setup --- p.67 / appendix c numerical results --- p.69

Thesis (Ph. D.)--University of California, Riverside, 2009. / Includes abstract. Available via ProQuest Digital Dissertations. Title from first page of PDF file (viewed March 12, 2010). Includes bibliographical references (p. ). Also issued in print.

Urban freeway demand that frequently exceeds capacity has caused many agencies to consider many options to reduce congestion. A series of solutions that falls under the Active Traffic Management (ATM) banner have shown promising potential. Perhaps the most popular ATM strategy is ramp metering. Ramp metering involves limiting the access of vehicles to freeways at an entrance ramp. By doing this, freeway throughput, speeds, and travel time reliability can be increased, while the number of traffic incidents can be decreased. This study examines the application of an innovative ramp metering strategy, Intersection Ramp Metering (IRM), at a section of Loop 1 in Austin, TX. IRM implements the ramp metering function at the intersection immediately upstream of the entrance ramp, rather than on the ramp itself. A microsimulation analysis of this application is performed in VISSIM, and the results confirm that freeway throughput (+10%), and system average travel time (-14%), can be improved, as well as several other performance measures. / text