This thesis investigates the important potential of Cooperative Intelligent Transport Systems (C-ITS) onmobility and society. C-ITS appears as a promising solution to reinvent transportation and become avital part of the ever-evolving environment as developments in technology continue to change the world.The goal of this study is to investigate how C-ITS can enhance and promote various forms of mobility.It additionally investigates at how C-ITS applications and policy objectives interact, highlighting C-ITS’contribution to the development of a sustainable society.A thorough examination of the current literature, case studies, and pertinent policies was conductedin order to analyse the possible advantages and difficulties related to C-ITS in detail. In order toprovide seamless communication and interaction between C-ITS systems and other devices, the researchemphasizes the importance of early integration and adoption of C-ITS as a solution. It also emphasizesthe need for standardization, interoperability, and collaborative efforts among stakeholders.Findings demonstrate that C-ITS has the capacity to support policies aimed at improving transportationsystems and mobility in the cities. C-ITS usage has enormous potential for influencing society andmobility. C-ITS reduces accidents while enhancing road safety through real-time communication. Byenhancing traffic flow and promoting alternative modes of transportation, it supports environmentalsustainability. It also has secondary effects such as reducing pollutants and improving air andnoise quality. Through the integration of numerous mobility choices and the provision of real-timeinformation, C-ITS improves accessibility. For implementation to be successful, privacy and securityissues as well as economic reasons must be taken into consideration. To solve the issues posed byconcerns about data privacy, security, and economic factors, however, strong policies, legislation,and safe data processing techniques are needed. C-ITS has the potential to help create a future oftransportation that is safer, more environmentally friendly, and more effective.In the concluding part, the paper suggests numerous possibilities for C-ITS research going forward.It advises combining policies and guiding documents to offer a clearer strategy for utilizing C-ITSsuccessfully. Additionally, creating more complex mathematical models that include equations can helpus comprehend the importance of the variables better. Iterative procedures integrated into detailedmodels allow for the comparison of many scenarios, addressing the various desires of stakeholders andexperts. Additionally, combining C-ITS with Vehicle-to-Everything (VoT) systems offer a chance toinvestigate the real advantages and make it simpler to make comparisons with other variables. Furtherresearch should be carried out on the likelihood of developing an automated mobility system.

Traffic safety has become the first concern in the transportation area. Crashes have cause extensive human and economic losses. With the objective of reducing crash occurrence and alleviating crash injury severity, major efforts have been dedicated to reveal the hazardous factors that affect crash occurrence at both the aggregate (targeting crash frequency per segment, intersection, etc.,) and disaggregate levels (analyzing each crash event). The aggregate traffic safety studies, mainly developing safety performance functions (SPFs), are being conducted for the purpose of unveiling crash contributing factors for the interest locations. Results of the aggregate traffic safety studies can be used to identify crash hot spots, calculate crash modification factors (CMF), and improve geometric characteristics. Aggregate analyses mainly focus on discovering the hazardous factors that are related to the frequency of total crashes, of specific crash type, or of each crash severity level. While disaggregate studies benefit from the reliable surveillance systems which provide detailed real-time traffic and weather data. This information could help in capturing microlevel influences of the hazardous factors which might lead to a crash. The disaggregate traffic safety models, also called real-time crash risk evaluation models, can be used in monitoring crash hazardousness with the real-time field data fed in. One potential use of real-time crash risk evaluation models is to develop Variable Speed Limits (VSL) as a part of a freeway management system. Models have been developed to predict crash occurrence to proactively improve traffic safety and prevent crash occurrence. iv In this study, first, aggregate safety performance functions were estimated to unveil the different risk factors affecting crash occurrence for a mountainous freeway section. Then disaggregate real-time crash risk evaluation models have been developed for the total crashes with both the machine learning and hierarchical Bayesian models. Considering the need for analyzing both aggregate and disaggregate aspects of traffic safety, systematic multi-level traffic safety studies have been conducted for single- and multi-vehicle crashes, and weekday and weekend crashes. Finally, the feasibility of utilizing a VSL system to improve traffic safety on freeways has been investigated. This research was conducted based on data obtained from a 15-mile mountainous freeway section on I-70 in Colorado. The data contain historical crash data, roadway geometric characteristics, real-time weather data, and real-time traffic data. Real-time weather data were recorded by 6 weather stations installed along the freeway section, while the real-time traffic data were obtained from the Remote Traffic Microwave Sensor (RTMS) radars and Automatic Vechicle Identification (AVI) systems. Different datasets have been formulated from various data sources, and prepared for the multi-level traffic safety studies. In the aggregate traffic safety investigation, safety performance functions were developed to identify crash occurrence hazardous factors. For the first time real-time weather and traffic data were used in SPFs. Ordinary Poisson model and random effects Poisson models with Bayesian inference approach were employed to reveal the effects of weather and traffic related variables on crash occurrence. Two scenarios were considered: one seasonal based case and one crash type v based case. Deviance Information Criterion (DIC) was utilized as the comparison criterion; and the correlated random effects Poisson models outperform the others. Results indicate that weather condition variables, especially precipitation, play a key role in the safety performance functions. Moreover, in order to compare with the correlated random effects Poisson model, Multivariate Poisson model and Multivariate Poisson-lognormal model have been estimated. Conclusions indicate that, instead of assuming identical random effects for the homogenous segments, considering the correlation effects between two count variables would result in better model fit. Results from the aggregate analyses shed light on the policy implication to reduce crash frequencies. For the studied roadway segment, crash occurrence in the snow season have clear trends associated with adverse weather situations (bad visibility and large amount of precipitation); weather warning systems can be employed to improve road safety during the snow season. Furthermore, different traffic management strategies should be developed according to the distinct seasonal influence factors. In particular, sites with steep slopes need more attention from the traffic management center and operators especially during snow seasons to control the excess crash occurrence. Moreover, distinct strategy of freeway management should be designed to address the differences between single- and multi-vehicle crash characteristics. In addition to developing safety performance functions with various modeling techniques, this study also investigates four different approaches of developing informative priors for the independent variables. Bayesian inference framework provides a complete and coherent way to balance the empirical data and prior expectations; merits of these informative priors have been tested along with two types of Bayesian hierarchical models (Poisson-gamma and Poisson- vi lognormal models). Deviance Information Criterion, R-square values, and coefficients of variance for the estimations were utilized as evaluation measures to select the best model(s). Comparisons across the models indicate that the Poisson-gamma model is superior with a better model fit and it is much more robust with the informative priors. Moreover, the two-stage Bayesian updating informative priors provided the best goodness-of-fit and coefficient estimation accuracies. In addition to the aggregate analyses, real-time crash risk evaluation models have been developed to identify crash contributing factors at the disaggregate level. Support Vector Machine (SVM), a recently proposed statistical learning model and Hierarchical Bayesian logistic regression models were introduced to evaluate real-time crash risk. Classification and regression tree (CART) model has been developed to select the most important explanatory variables. Based on the variable selection results, Bayesian logistic regression models and SVM models with different kernel functions have been developed. Model comparisons based on receiver operating curves (ROC) demonstrate that the SVM model with Radial basis kernel function outperforms the others. Results from the models demonstrated that crashes are likely to happen during congestion periods (especially when the queuing area has propagated from the downstream segment); high variation of occupancy and/or volume would increase the probability of crash occurrence. Moreover, effects of microscopic traffic, weather, and roadway geometric factors on the occurrence of specific crash types have been investigated. Crashes have been categorized as rear- vii end, sideswipe, and single-vehicle crashes. AVI segment average speed, real-time weather data, and roadway geometric characteristics data were utilized as explanatory variables. Conclusions from this study imply that different active traffic management (ATM) strategies should be designed for three- and two-lane roadway sections and also considering the seasonal effects. Based on the abovementioned results, real-time crash risk evaluation models have been developed separately for multi-vehicle and single-vehicle crashes, and weekday and weekend crashes. Hierarchical Bayesian logistic regression models (random effects and random parameter logistic regression models) have been introduced to address the seasonal variations, crash unit level’s diversities, and unobserved heterogeneity caused by geometric characteristics. For the multi-vehicle crashes: congested conditions at downstream would contribute to an increase in the likelihood of multi-vehicle crashes; multi-vehicle crashes are more likely to occur during poor visibility conditions and if there is a turbulent area that exists downstream. Drivers who are unable to reduce their speeds timely are prone to causing rear-end crashes. While for the singlevehicle crashes: slow moving traffic platoons at the downstream detector of the crash occurrence locations would increase the probability of single-vehicle crashes; large variations of occupancy downstream would also increase the likelihood of single-vehicle crash occurrence. Substantial efforts have been dedicated to revealing the hazardous factors that affect crash occurrence from both the aggregate and disaggregate level in this study, however, findings and conclusions from these research work need to be transferred into applications for roadway design and freeway management. This study further investigates the feasibility of utilizing Variable Speed Limits (VSL) system, one key part of ATM, to improve traffic safety on freeways. A proactive traffic safety improvement VSL control algorithm has been proposed. First, an viii extension of the traffic flow model METANET was employed to predict traffic flow while considering VSL’s impacts on the flow-density diagram; a real-time crash risk evaluation model was then estimated for the purpose of quantifying crash risk; finally, the optimal VSL control strategies were achieved by employing an optimization technique of minimizing the total predicted crash risks along the VSL implementation area. Constraints were set up to limit the increase of the average travel time and differences between posted speed limits temporarily and spatially. The proposed VSL control strategy was tested for a mountainous freeway bottleneck area in the microscopic simulation software VISSIM. Safety impacts of the VSL system were quantified as crash risk improvements and speed homogeneity improvements. Moreover, three different driver compliance levels were modeled in VISSIM to monitor the sensitivity of VSL’s safety impacts on driver compliance levels. Conclusions demonstrate that the proposed VSL system could effectively improve traffic safety by decreasing crash risk, enhancing speed homogeneity, and reducing travel time under both high and moderate driver compliance levels; while the VSL system does not have significant effects on traffic safety enhancement under the low compliance scenario. Future implementations of VSL control strategies and related research topics were also discussed.

Presently, the United States has yet to achieve the 1991 Intermodal Surface Transportation Efficiency Act's (ISTEA) goal of creating a seamless intermodal transportation system. In addition to the dearth of connections, the nation's poor transportation information systems limit intercity intermodal transportation. Travelers lack awareness of available transportation options and face too many separate portals for trip planning that both consume time and present inadequate information. This paper posits that the creation of an efficient and extensive web-based door-to-door intermodal trip planner can solve these problems. The proposed system will present travelers with a single portal to meet all their trip planning needs. Upon selecting specific trips, travelers can then decide to be directed to operators to make a purchase. The system will include operators from the major modal groups including intercity buses, intercity rail, commuter rail, transit, and airlines. It will also include taxis due to the disjointed nature of the US public transportation system and the need to connect users who are far from stations. The requirements to create this trip planner are explored, including the support systems, potential legal issues, and suitable entities for administration and management. A survey of 39 transportation system users revealed the existence of redundant and inadequate trip planners and that the lack of sufficient information on public transportation options is driving travelers to private vehicles for shorter distances even for those who prefer public means of transportation. Analysis of the costs and benefits of implementing the proposed system is drawn from interviews with key personnel within the transportation industry, and a review of nationwide trip planners in European countries. Finally, a roadmap is presented on how best to implement the system with inputs from both the public and private sector. Recommendations include the establishment of an industry-wide data standard, a national interagency database, and a cooperative structure that entices major players within each mode to participate in the system. Also suggested are incentives from the DOT and interested private sector members to encourage more operators to participate in the system. / Master of Science

Transportation Engineers have recently begun to incorporate statistical and machine learning approaches to solving difficult problems, mainly due to the vast quantities of data collected that is stochastic (sensors, video, and human collected). In transportation engineering, a transportation system is often denoted by jurisdiction boundaries and evaluated as such. However, it is ultimately defined by the consideration of the analyst in trying to answer the question of interest. In this dissertation, a transportation system located in Jackson, Wyoming under the jurisdiction of the Grand Teton National Park and recognized as the Moose-Wilson Corridor is evaluated to identify transportation-related factors that influence its operational performance. The evaluation considers its unique prevalent conditions and takes into account future management strategies. The dissertation accomplishes this by detailing four distinct aspects in individual chapters; each chapter is a standalone manuscript with detailed introduction, purpose, literature review, findings, and conclusion. Chapter 1 provides a general introduction and provides a summary of Chapters 2 – 6. Chapter 2 focuses on evaluating the operational performance of the Moose-Wilson Corridor's entrance station, where queueing performance and arrival and probability mass functions of the vehicle arrival rates are determined. Chapter 3 focuses on the evaluation of a parking system within the Moose-Wilson Corridor in a popular attraction known as the Laurance S. Rockefeller Preserve, in which the system's operational performance is evaluated, and a probability mass function under different arrival and service rates are provided. Chapter 4 provides a data science approach to predicting the probability of vehicles stopping along the Moose-Wilson Corridor. The approach is a machine learning classification methodology known as "decision tree." In this study, probabilities of stopping at attractions are predicted based on GPS tracking data that include entrance location, time of day and stopping at attractions. Chapter 5 considers many of the previous findings, discusses and presents a developed tool which utilizes a Bayesian methodology to determine the posterior distributions of observed arrival rates and service rates which serve as bounds and inputs to an Agent-Based Model. The Agent-Based Model represents the Moose-Wilson Corridor under prevailing conditions and considers some of the primary operational changes in Grand Teton National Park's comprehensive management plan for the Moose-Wilson Corridor. The implementation of an Agent-Based Model provides a flexible platform to model multiple aspects unique to a National Park, including visitor behavior and its interaction with wildlife. Lastly, Chapter 6 summarizes and concludes the dissertation. / Doctor of Philosophy / In this dissertation, a transportation system located in Jackson, Wyoming under the jurisdiction of the Grand Teton National Park and recognized as the Moose-Wilson Corridor is evaluated to identify transportation-related factors that influence its operational performance. The evaluation considers its unique prevalent conditions and takes into account future management strategies. Furthermore, emerging analytical strategies are implemented to identify and address transportation system operational concerns. Thus, in this dissertation, decision support tools for the evaluation of a unique system in a National Park are presented in four distinct manuscripts. The manuscripts cover traditional approaches that breakdown and evaluate traffic operations and identify mitigation strategies. Additionally, emerging strategies for the evaluation of data with machine learning approaches are implemented on GPS-tracks to determine vehicles stopping at park attractions. Lastly, an agent-based model is developed in a flexible platform to utilize previous findings and evaluate the Moose-Wilson corridor while considering future policy constraints and the unique natural interactions between visitors and prevalent ecological and wildlife.

This thesis describes the current state of development of the Flexible Low-cost Automated Scaled Highway (FLASH) laboratory at the Virginia Tech Transportation Institute (VTTI). The FLASH lab and the scale model cars contained therein provide a testbed for the small scale development stage of intelligent transportation systems (ITS). In addition, the FLASH lab serves as a home to the prototype display being developed for an educational museum exhibit. This thesis also gives details of the path following lateral controller implemented on the FLASH car. The controller was developed using the kinematic model for a wheeled robot. The global model is converted into the path coordinate model so that only local variables are needed. then the path coordinate model is converted into chained form and a controller is given to perform path following. The path coordinate model introduces a new parameter to the system: the curvature of the path. Thus, it is necessary to provide the path's curvature value to the controller. Because of the environment in which the car is operating, the curvature values are known a priori. Several online methods for determining the curvature are developed. A MATLAB simulation environment was created with which to test the above algorithms. The simulation uses the kinematic model to show the car's behavior and implements the sensors and controller as closely as possible to the actual system. The implementation of the lateral controller in hardware is discussed. The vehicle platform is described and the harware and software architecture detailed. The car described is capable of operating manually and autonomously. In autonomous mode, several sensors are utilized including: infrared, magnetic, ultrasound, and image based technology. The operation of each sensor type is described and the information received by the processor from each is discussed. / Master of Science

Aujourd'hui c'est un fait, nous vivons dans un monde où les enjeux écologiques, économiques et sociétaux sont de plus en plus pressants. Au croisement des différentes lignes directrices envisagées pour répondre à ces problèmes, une vision plus précise de la mobilité humaine est un axe central et majeur, qui a des répercussions sur tous les domaines associés tels que le transport, les sciences sociales, l'urbanisme, les politiques d'aménagement, l'écologie, etc. C'est par ailleurs dans un contexte de contraintes budgétaires fortes que les principaux acteurs de la mobilité sur les territoires cherchent à rationaliser les services de transport, et les déplacements des individus. La mobilité humaine est donc un enjeu stratégique aussi bien pour les collectivités locales que pour les usagers, qu'il faut savoir observer, comprendre, et anticiper.Cette étude de la mobilité passe avant tout par une observation précise des déplacements des usagers sur les territoires. Aujourd'hui les acteurs de la mobilité se tournent principalement vers l'utilisation massive des données utilisateurs. L'utilisation simultanée de données multi-sources, multi-modales, et multi-échelles permet d'entrevoir de nombreuses possibilités, mais cette dernière présente des défis technologiques et scientifiques majeurs. Les modèles de mobilité présentés dans la littérature sont ainsi trop souvent axés sur des zones d'expérimentation limitées, en utilisant des données calibrées, etc. et leur application dans des contextes réels, et à plus large échelle est donc discutable. Nous identifions ainsi deux problématiques majeures qui permettent de répondre à ce besoin d'une meilleure connaissance de la mobilité humaine, mais également à une meilleure application de cette connaissance. La première problématique concerne l'extraction d'informations de mobilité à partir de la fusion de données hétérogènes. La seconde problématique concerne la pertinence de cette fusion dans un contexte réel, et à plus large échelle. Nous apportons différents éléments de réponses à ces problématiques dans cette thèse. Tout d'abord en présentant deux modèles de fusion de données, qui permettent une extraction d'informations pertinentes. Puis, en analysant l'application de ces deux modèles au sein du projet ANR Norm-Atis.Dans cette thèse, nous suivons finalement le développement de toute une chaine de processus. En commençant par une étude de la mobilité humaine, puis des modèles de mobilité, nous présentons deux modèles de fusion de données, et nous analysons leur pertinence dans un cas concret. Le premier modèle que nous proposons permet d'extraire 12 comportements types de mobilité. Il est basé sur un apprentissage non-supervisé de données issues de la téléphonie mobile. Nous validons nos résultats en utilisant des données officielles de l'INSEE, et nous déduisons de nos résultats, des comportements dynamiques qui ne peuvent pas être observés par les données de mobilité traditionnelles. Ce qui est une forte valeur-ajoutée de notre modèle. Le second modèle que nous proposons permet une désagrégation des flux de mobilité en six motifs de mobilité. Il se base sur un apprentissage supervisé des données issues d'enquêtes de déplacements ainsi que des données statiques de description du sursol. Ce modèle est appliqué par la suite aux données agrégés au sein du projet Norm-Atis. Les temps de calculs sont suffisamment performants pour permettre une application de ce modèle dans un contexte temps-réel. / Today it is a fact that we live in a world where ecological, economic and societal issues are increasingly pressing. At the crossroads of the various guidelines envisaged to address these problems, a more accurate vision of human mobility is a central and major axis, which has repercussions on all related fields such as transport, social sciences, urban planning, management policies, ecology, etc. It is also in the context of strong budgetary constraints that the main actors of mobility on the territories seek to rationalize the transport services and the movements of individuals. Human mobility is therefore a strategic challenge both for local communities and for users, which must be observed, understood and anticipated.This study of mobility is based above all on a precise observation of the movements of users on the territories. Nowadays mobility operators are mainly focusing on the massive use of user data. The simultaneous use of multi-source, multi-modal, and multi-scale data opens many possibilities, but the latter presents major technological and scientific challenges. The mobility models presented in the literature are too often focused on limited experimental areas, using calibrated data, etc., and their application in real contexts and on a larger scale is therefore questionable. We thus identify two major issues that enable us to meet this need for a better knowledge of human mobility, but also to a better application of this knowledge. The first issue concerns the extraction of mobility information from heterogeneous data fusion. The second problem concerns the relevance of this fusion in a real context, and on a larger scale. These issues are addressed in this dissertation: the first, through two data fusion models that allow the extraction of mobility information, the second through the application of these fusion models within the ANR Norm-Atis project.In this thesis, we finally follow the development of a whole chain of processes. Starting with a study of human mobility, and then mobility models, we present two data fusion models, and we analyze their relevance in a concrete case. The first model we propose allows to extract 12 types of mobility behaviors. It is based on an unsupervised learning of mobile phone data. We validate our results using official data from the INSEE, and we infer from our results, dynamic behaviors that can not be observed through traditional mobility data. This is a strong added-value of our model. The second model operates a mobility flows decompositoin into six mobility purposes. It is based on a supervised learning of mobility surveys data and static data from the land use. This model is then applied to the aggregated data within the Norm-Atis project. The computing times are sufficiently powerful to allow an application of this model in a real-time context.

Intelligent Transportation System (ITS) provides a great platform for the planners to reduce environmental externalities from auto. We now have access to real time data. We have been using shortest path to provide route choice to the user. But we have the potential to add more variables in choosing routes. Real time data can be used to measure carbon di-oxide emission during a trip. Also, fuel efficiency can be measured using the real time data. Planners should use this potential of ITS to reduce the environmental impact. This paper thus try to evaluate if considering three variables shortest path, environmental impact and fuel efficiency together instead of only shortest path will change the route choice or not. It provides case studies on different types of routes and between different sets of origin /destination to evaluate the combined influence of these three variables on route choice.

The issue that this study addresses is the public transportations difficulty to adapt and keep up with the continuously digitizing society. To address this problem, the purpose of this study will be to investigate what could be a contribution to the further improvements within public transportation systems. The study is established alongside with the recently started “Welcome onboard” project, where the purpose is to develop and extend the public transportation as we know it today. The aim is to through five different functional areas urge the utility, where crowdsourcing will play a central role. We are distinguishing three public transport authorities attitudes towards the different functions of the project. We will investigate the attitudes towards the Welcome  onboardprojects functions and if they could be a contribution to the RKM-companies future development. Furthermore, this thesis will also investigate how the RKM-companies would grade the different advantages of different functional areas, and in a extend what would bevital for an future implementation. The empirical data collection consist of interviews of informants with a deep understanding ofthe development of the public transportations. First we interviewed two key persons of the Welcome onboard project, to get a deep understanding of the project. Later on we interviewed key persons at Värmlandstrafiken and Västtrafik, that are two different regional public transportation authorities. To strengthen our results we also chose to interview Samtrafiken, which task are to develop collaborations and offer services within information and ticket solutions for the public transport industry. This is made in order to benefit both traffic the RKM and the travelers.  All the interviews were transcribed and analyzed, later the empirical data collection could be compared through different cases. These cases were categorized by information about the company that the interviewees where representing and the answers regarding the attitudes towards the “Welcome onboard”-project and its functions. The result from the thesis will give an understanding of Västtrafik, Värmlandstrafiken and Samtrafikens perspective of the functions and attitudes towards the further development of public transportation.

Due to the degree of complexity related to measuring the advantage of establishing Electronic Toll Collection (ETC) systems, literature generally stops short of modeling an all-inclusive set of benefits of the system. In this research, a model that incorporates the impact on both the users and the society as a whole and evaluates the financial benefits over the lifespan of the ETC investment is developed. Most of the values for the parameters used for calculating the benefits are taken from Federal Highway Administration (FHWA) and from similar studies conducted by transportation agencies, which is the setting that has motivated the current research. These parameters are national averages and not region specific. The model will serve as a decision making tool to determine the number of ETC lanes over the manual and automatic lanes. The model has been used for toll plazas with different number of lanes to study the financial value of the benefits due to the ETC deployment. It is also used to study the effect of the traffic flow on the total benefits and recommendation has been made with respect to the time for the ETC deployment.

Title from PDF of title page. / Document formatted into pages; contains 71 pages. / Thesis (M.S.I.E.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Due to the degree of complexity related to measuring the advantage of establishing Electronic Toll Collection (ETC) systems, literature generally stops short of modeling an all-inclusive set of benefits of the system. In this research, a model that incorporates the impact on both the users and the society as a whole and evaluates the financial benefits over the lifespan of the ETC investment is developed. Most of the values for the parameters used for calculating the benefits are taken from Federal Highway Administration (FHWA) and from similar studies conducted by transportation agencies, which is the setting that has motivated the current research. These parameters are national averages and not region specific. The model will serve as a decision making tool to determine the number of ETC lanes over the manual and automatic lanes. / ABSTRACT: The model has been used for toll plazas with different number of lanes to study the financial value of the benefits due to the ETC deployment. It is also used to study the effect of the traffic flow on the total benefits and recommendation has been made with respect to the time for the ETC deployment. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.