An improved framework for dynamic origin-destination (O-D) matrix estimation

This dissertation aims to improve the performance of existing assignment-based dynamic origin-destination (O-D) matrix estimation models to successfully apply Intelligent Transportation Systems (ITS) strategies for the purposes of traffic congestion relief and dynamic traffic assignment (DTA) in transportation network modeling. The methodology framework has two advantages over the existing assignment-based dynamic O-D matrix estimation models. First, it combines an initial O-D estimation model into the estimation process to provide a high confidence level of initial input for the dynamic O-D estimation model, which has the potential to improve the final estimation results and reduce the associated computation time. Second, the proposed methodology framework can automatically convert traffic volume deviation to traffic density deviation in the objective function under congested traffic conditions. Traffic density is a better indicator for traffic demand than traffic volume under congested traffic condition, thus the conversion can contribute to improving the estimation performance. The proposed method indicates a better performance than a typical assignment-based estimation model (Zhou et al., 2003) in several case studies. In the case study for I-95 in Miami-Dade County, Florida, the proposed method produces a good result in seven iterations, with a root mean square percentage error (RMSPE) of 0.010 for traffic volume and a RMSPE of 0.283 for speed. In contrast, Zhou's model requires 50 iterations to obtain a RMSPE of 0.023 for volume and a RMSPE of 0.285 for speed. In the case study for Jacksonville, Florida, the proposed method reaches a convergent solution in 16 iterations with a RMSPE of 0.045 for volume and a RMSPE of 0.110 for speed, while Zhou's model needs 10 iterations to obtain the best solution, with a RMSPE of 0.168 for volume and a RMSPE of 0.179 for speed. The successful application of the proposed methodology framework to real road networks demonstrates its ability to provide results both with satisfactory accuracy and within a reasonable time, thus establishing its potential usefulness to support dynamic traffic assignment modeling, ITS systems, and other strategies.

Colorado's Piceance Basin: Variation in the local public finance effects of oil and gas development

A large increase in natural gas production occurred in western Colorado’s Piceance basin in the mid- to late-2000s, generating a surge in population, economic activity, and heavy truck traffic in this rural region. We describe the fiscal effects related to this development for two county governments: Garfield and Rio Blanco, and two city governments: Grand Junction and Rifle. Counties maintain rural road networks in Colorado, and Garfield County’s ability to fashion agreements with operators to repair roads damaged during operations helped prevent the types of large new costs seen in Rio Blanco County, a neighboring county with less government capacity and where such agreements were not made. Rifle and Grand Junction experienced substantial oil- and gas-driven population growth, with greater challenges in the smaller, more isolated, and less economically diverse city of Rifle. Lessons from this case study include the value of crafting road maintenance agreements, fiscal risks for small and geographically isolated communities experiencing rapid population growth, challenges associated with limited infrastructure, and the desirability of flexibility in the allocation of oil- and gas-related revenue.

Network Development with Bicycle Lanes

Bicycling as an active mode of transport can offer great individual and societal benefits. Allocating space for bicycle facilities is the key to promoting cycling as bicyclists perceive better safety and convenience in separate bikeways. In this thesis, a method is proposed for optimizing the selection and scheduling of capacity enhancements in road networks while also optimizing the allocation of road space to bicycle lanes. The goal is to determine what fraction of the available space should be allocated to bicycles, as the network evolves, in order to minimize the present value of the total cost of the system cost. The allocation method is combined with a genetic algorithm to select and schedule road expansion projects under certain budget constraints.

ASSESSMENT OF ASPEN AND NORTHERN HARDWOODS EXTENT IN THIRTY THREE COUNTIES OF UPPER/LOWER MICHIGAN

The importance of the United States' wood and wood byproducts as biomass feedstocks is increasing as the concern about security and sustainability of global energy production continues to rise. Thus, second generation woody feedstock sources in Michigan, e.g., hybrid poplar and hybrid willow (Populus spp.), are viewed as a potential source of biomass for the proposed biofuel ethanol production plant in Kinross, MI. It is important to gain an understanding of the spatial distribution of current feedstock sources, harvesting accessibility via the transportation infrastructure and land ownerships in order to ensure long-term feedstock extent. This research provides insights into the current extent of aspen and northern hardwoods, and an assessment of potential for expanding the area of these feedstock sources based on pre-European settlement conditions. A geographic information system (GIS) was developed to compile available geospatial data for 33 counties located within 150 miles of the Kinross facility. These include present day and pre-European settlement land use/cover, soils, road infrastructure, and land ownerships. The results suggest that a significant amount of northern hardwoods has been converted to other land use/cover types since European settlement, and the "scattering" of aspen stands has increased. Furthermore, a significant amount of woody biomass is available in close proximity to the existing road network, which can be effectively utilized as feedstock. Potential aspen and northern hardwoods restoration areas are identified in the vicinity of road networks which can be used for future woody feedstock production.

INVESTIGATIVE COMPARISON OF TANK AND RECOVERED ASPHALT SAMPLES THROUGH PHYSICAL PERFORMANCE TESTING AND CHEMICAL ANALYSIS

Despite the development of improved performance test protocols by renowned researchers, there are still road networks which experience premature cracking and failure. One area of major concern in asphalt science and technology, especially in cold regions in Canada is thermal (low temperature) cracking. Usually right after winter periods, severe cracks are seen on poorly designed road networks. Quality assurance tests based on improved asphalt performance protocols have been implemented by government agencies to ensure that roads being constructed are at the required standard but asphalt binders that pass these quality assurance tests still crack prematurely. While it would be easy to question the competence of the quality assurance test protocols, it should be noted that performance tests which are being used and were repeated in this study, namely the extended bending beam rheometer (EBBR) test, double edge-notched tension test (DENT), dynamic shear rheometer (DSR) test and X-ray fluorescence (XRF) analysis have all been verified and proven to successfully predict asphalt pavement behaviour in the field. Hence this study looked to probe and test the quality and authenticity of the asphalt binders being used for road paving. This study covered thermal cracking and physical hardening phenomenon by comparing results from testing asphalt binder samples obtained from the storage ‘tank’ prior to paving (tank samples) and recovered samples for the same contracts with aim of explaining why asphalt binders that have passed quality assurance tests are still prone to fail prematurely. The study also attempted to find out if the short testing time and automated procedure of torsion bar experiments can replace the established but tedious procedure of the EBBR. In the end, it was discovered that significant differences in performance and composition exist between tank and recovered samples for the same contracts. Torsion bar experimental data also indicated some promise in predicting physical hardening.

Assessing landscape functional connectivity in a forest carnivore using path selection functions

Context Understanding connectivity patterns in relation to habitat fragmentation is essential to landscape management. However, connectivity is often judged from expert opinion or species occurrence patterns, with very few studies considering the actual movements of individuals. Path selection functions provide a promising tool to infer functional connectivity from animal movement data, but its practical application remains scanty. Objectives We aimed to describe functional connectivity patterns in a forest carnivore using path-level analysis, and to explore how connectivity is affected by land cover patterns and road networks. Methods We radiotracked 22 common genets in a mixed forest-agricultural landscape of southern Portugal. We developed path selection functions discriminating between observed and random paths in relation to landscape variables. These functions were used together with land cover information to map conductance surfaces. Results Genets moved preferentially within forest patches and close to riparian habitats. Functional connectivity declined with increasing road density, but increased with the proximity of culverts, viaducts and bridges. Functional connectivity was favoured by large forest patches, and by the presence of riparian areas providing corridors within open agricultural land. Roads reduced connectivity by dissecting forest patches, but had less effect on riparian corridors due to the presence of crossing structures. Conclusions Genet movements were jointly affected by the spatial distribution of suitable habitats, and the presence of a road network dissecting such habitats and creating obstacles in areas otherwise permeable to animal movement. Overall, the study showed the value of path-level analysis to assess functional connectivity patterns in human-modified landscapes.

Stakeholder engagement by governance networks : a study of stakeholder engagement by road delivery network in Queensland

This thesis explores how governance networks prioritise and engage with their stakeholders, by studying three exemplars of “Regional Road Group” governance networks in Queensland, Australia. In the context of managing regionally significant road works programs, stakeholder prioritisation is a complex activity which is unlikely to influence interactions with stakeholders outside of the network. However, stakeholder priority is more likely to influence stakeholder interactions within the networks themselves. Both stakeholder prioritisation and engagement are strongly influenced by the way that the networks are managed, and in particular network operating rules and continuing access to resources.

Security and Privacy in Vehicular Ad-Hoc Networks : Survey and the Road Ahead

Vehicular Ad-hoc Networks (VANETs) can make roads safer, cleaner, and smarter. It can offer a wide range of services, which can be safety and non-safety related. Many safety-related VANETs applications are real-time and mission critical, which would require strict guarantee of security and reliability. Even non-safety related multimedia applications, which will play an important role in the future, will require security support. Lack of such security and privacy in VANETs is one of the key hindrances to the wide spread implementations of it. An insecure and unreliable VANET can be more dangerous than the system without VANET support. So it is essential to make sure that “life-critical safety” information is secure enough to rely on. Securing the VANETs along with appropriate protection of the privacy drivers or vehicle owners is a very challenging task. In this work we summarize the attacks, corresponding security requirements and challenges in VANETs. We also present the most popular generic security policies which are based on prevention as well detection methods. Many VANETs applications require system-wide security support rather than individual layer from the VANETs’ protocol stack. In this work we will review the existing works in the perspective of holistic approach of security. Finally, we will provide some possible future directions to achieve system-wide security as well as privacy-friendly security in VANETs.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

On Transport Service Selection in Intermodal Rail/Road Distribution Networks

Intermodal rail/road freight transport constitutes an alternative to long-haul road transport for the distribution of large volumes of goods. The paper introduces the intermodal transportation problem for the tactical planning of mode and service selection. In rail mode, shippers either book train capacity on a per-unit basis or charter block trains completely. Road mode is used for short-distance haulage to intermodal terminals and for direct shipments to customers. We analyze the competition of road and intermodal transportation with regard to freight consolidation and service cost on a model basis. The approach is applied to a distribution system of an industrial company serving customers in eastern Europe. The case study investigates the impact of transport cost and consolidation on the optimal modal split.

Performance analysis of DSRC priority mechanism for road safety applications in vehicular networks

Dedicated short range communications (DSRC) has been regarded as one of the most promising technologies to provide robust communications for large scale vehicle networks. It is designed to support both road safety and commercial applications. Road safety applications will require reliable and timely wireless communications. However, as the medium access control (MAC) layer of DSRC is based on the IEEE 802.11 distributed coordination function (DCF), it is well known that the random channel access based MAC cannot provide guaranteed quality of services (QoS). It is very important to understand the quantitative performance of DSRC, in order to make better decisions on its adoption, control, adaptation, and improvement. In this paper, we propose an analytic model to evaluate the DSRC-based inter-vehicle communication. We investigate the impacts of the channel access parameters associated with the different services including arbitration inter-frame space (AIFS) and contention window (CW). Based on the proposed model, we analyze the successful message delivery ratio and channel service delay for broadcast messages. The proposed analytical model can provide a convenient tool to evaluate the inter-vehicle safety applications and analyze the suitability of DSRC for road safety applications.

Adaptive rate control of dedicated short range communications based vehicle networks for road safety applications

Dedicated Short Range Communication (DSRC) is a promising technique for vehicle ad-hoc network (VANET) and collaborative road safety applications. As road safety applications require strict quality of services (QoS) from the VANET, it is crucial for DSRC to provide timely and reliable communications to make safety applications successful. In this paper we propose two adaptive message rate control algorithms for low priority safety messages, in order to provide highly available channel for high priority emergency messages while improve channel utilization. In the algorithms each vehicle monitors channel loads and independently controls message rate by a modified additive increase and multiplicative decrease (AIMD) method. Simulation results demonstrated the effectiveness of the proposed rate control algorithms in adapting to dynamic traffic load.

Two-phases control of DSRC Vehicle networks for road intersection coexisting safety applications

In this paper we propose a two phases control method for DSRC vehicle networks at road intersection, where multiple road safety applications may coexist. We consider two safety applications, emergency safety application with high priority and routine safety applications with low priority. The control method is designed to provide high availability and low latency for emergency safety applications while leave as much as possible bandwidth for routine applications. It is expected to be capable of adapting to changing network conditions. In the first phase of the method we use a simulation based offline approach to find out the best configurations for message rate and MAC layer parameters for given numbers of vehicles. In the second phase we use the configurations identified by simulations at roadside access point (AP) for system operation. A utilization function is proposed to balance the QoS performances provided to multiple safety applications. It is demonstrated that the proposed method can largely improve the system performance when compared to fixed control method.