Look ahead cruise control deals with the concept of using recorded topographic road data combined with a GPS to control vehicle speed. The purpose of this is to save fuel without a change in travel time for a given road. This thesis explores the sensitivity of different disturbances for look ahead systems. Two different systems are investigated, one using a simple precalculated speed trajectory without feedback and the second based upon a model predictive control scheme with dynamic programming as optimizing algorithm. Defect input data like bad positioning, disturbed angle data, faults in mass estimation and wrong wheel radius are discussed in this thesis. Also some investigations of errors in the environmental model for the systems are done. Simulations over real road profiles with two different types of quantization of the road slope data are done. Results from quantization of the angle data in the system are important since quantization will be unavoidable in an implementation of a topographic road map. The results from the simulations shows that disturbance of the fictive road profiles used results in quite large deviations from the optimal case. For the recorded real road sections however the differences are close to zero. Finally conclusions of how large deviations from real world data a look ahead system can tolerate are drawn.

No description available.

The European Commission has for some time indicated a desire to remove the remaining restrictions on cabotage. Such liberalization is expected to result in significant changes on some domestic road transport markets. The Scandinavian markets are among these and the local hauilers fear that the lower wages of Easter European hauliers will leave them out of business. In this thesis a scenario of full cabotage liberalization is assumed. Representatives from associations with interest within the Scandinavian road transport markets has been approached with a qualitative survey on carrier selection criteria. Respondents are asked to determine the importance of the criteria, as well as the relevance to the competition between Scandinavian and Eastern European hauliers. Finally they are asked to assess whether the advantage of each criterion lies with Scandinavian or Eastern European hauliers. The qualitative orientation of the survey is achieved though justification and follow up questions. Reliability of pickups and deliveries is still regarded the most important criteria. Also competitive prices received a high rating. It is concluded that the significant price difference between Scandinavian and Eastern European hauliers will benefit Eastern European hauliers considerably in the competition for the simple “from A to B” shipments. For more complicated shipments, entailing a higher level of service, communication and supply chain integration, the local hauliers are expected to stand their ground.

This thesis is focused on developing a robust algorithm for recognition of road signs including all stages of a machine vision system i.e. image acquisition, pre-processing, colour segmentation, labelling and classifi-cation. Images are acquired by two different imaging systems and noise removal is done by applying Mean filter. Furthermore, different colour segmentation methods are investigated to find out the most high-performance approach and after applying dynamic segmentation based on blue channel in YCbCr colour space, the obtained binary image is transferred to a personal computer through the developed PC software using standard serial port and further processing and classification is run on the PC. Histogram of Oriented Gradients (HOG) is used as the main feature for recognition of road signs and finally the classification task is fulfilled by employing hardware efficient Minimum Distance Classifier (MDC).

No description available.

Road safety is a major issue affecting the road sector. Road accidents remain a serious impediment to sustainable human development in many of the developing member countries (DMCs) of the Asian Development Bank (ADB). Road accidents continue to be an important social and economic problem in developing countries like India. Growth in the number of motor vehicles, poor enforcement of traffic safety regulations, poor quality of roads and vehicles, and inadequate public health infrastructures are some of the road safety problems facing in India. The object of this Thesis is to present a status report on the nature of the government policy towards the Activity plans implemented till now and which has to be implemented later for the reduction of road fatalities and for the safe roads, and also giving the guidelines for financing of remedial measures, institutional framework, physical characteristics of the road, traffic control and calming measures, road safety education and enforcement issues. The aim of the Activity plans is to analyze the present situation of road safety in India and to indicate main problems in individual sector of the Activity implemented by comparing and taking the examples of some of the ASEAN Region who are successed in implementing in the individual sectors. The effect of the programme to real safety situation is estimated, and further plans could be corrected if it is necessary. Implementation of the goals for the coming years to reduce the number of accidents at maximum extent and give people, the safe and the steady flow of traffic in India. The vision of a tremendous change next 5 to 10 years is based on a big potential for improvement and a joint effort of all involved groups on all levels of traffic safety, centrally coordinated by the National Road Safety Authorities. The Action Plan is deliberately divided into 14 key Sectors of activity in broadly the same way as the individual country road safety action plans. The sectors involve many different disciplines and a very wide range of multi sector activities but all are based on applying scientific, methodical approaches to the problem. At the end the thesis gives the recommendations and conclusion for the safe Roads in India

The Automated Manual Transmission system Opticruise is dependent on signals from sensors located in different parts of the Scania trucks. These signals are of different qualities and have different update frequencies. Some signals and quantities that are hard or impossible to measure are also of importance to this system. In this thesis a driveline observer for the purpose of signal improvement is developed and estimations of unknown quantities such as road incline and mass of the vehicle are performed. The outputs of the observer are produced at a rate of 100 Hz, and include in addition to the mass and road incline also the speed of the engine, output shaft of the gearbox, wheel and the torsion in the driveline. Further the use of an accelerometer and the advantages gained from using it in the observer are investigated. The outputs show an increased quality and much of the measurement noise is successfully removed without introducing any time delays. A simulation frequency of 100 Hz is possible, but some dependency toward the stiffness of the driveline is found. The observer manages to estimate the road slope accurately. With the use of an accelerometer the road slope estimation is further improved and a quickly converging mass estimation is obtained.

Scheduling of winter maintenance operations such as plowing or salting is a difficult and complex problem. Proper selection and timing of such operations is critical to their effectiveness, however scheduling decisions must often be made with strict time and resource limitations imposed upon them. A decision support system which analyses current road conditions and makes scheduling suggestions based on them would be a valuable step toward improving the quality of treatment, while simultaneously reducing the burden of scheduling on maintenance managers. This thesis proposes a real-time scheduling model based on an Operations Research framework that can be used by maintenance managers to develop and evaluate alternative resources allocation plans for winter road maintenance operations. The scheduling model is implemented as an Integer Linear Program and is solved using off-the-shelf software packages. The scheduling model takes into account a wide range of road and weather condition factors such as road network topology, road class, weather forecasts, and contractual service levels, and produces a vehicle dispatch schedule that is optimal with respect to operating costs and quality of service. A number of heuristics are also explored to aid in efficient approximations to this problem.

Chloride concentrations at the Greenbrook well field in Kitchener, Ontario, have been steadily increasing over the past several decades and may soon pose a threat to drinking water quality. Drinking water limits at some wells have already been exceeded. The Regional Municipality of Waterloo (RMOW) relies mainly on local groundwater resources for its drinking water supply, and the Greenbrook well field is the oldest of 50 municipal well fields contributing to this supply. Urban growth and the expansion of city limits over the years has surrounded the well field, placing it in a high risk area in need of protection. As such, protection of this water supply is essential until alternative sources can be found. Road salt has been identified as the prime source of the chloride contamination, and various management alternatives and remediation strategies are currently being studied. In order to characterize the behaviour of chloride in the subsurface, an understanding of the mechanisms that control travel of chloride to the water table and through the groundwater system is needed. For the first phase of this work, a 2-D variably-saturated flow and transport model (SWMS-2D) was used to evaluate the effect of seasonal fluctuation in chloride loading to a generic aquifer system. Chloride was applied over the surface of the model in seasonal pulses that correlated with temperature and precipitation. The model showed a dampening of the seasonal response with depth that lead to the conclusion that long-term transport models can neglect seasonal changes in solute loading. For the second phase of this work, a proven 3D finite element transport model (Waterloo Transport Code: WTC) was used to simulate road salt impacts to the well field. Road salt was applied over selected roads throughout the steady-state capture zone via a type 3 (Cauchy) boundary that varies both temporally and spatially with road type and location. After calibrating the model from 1945 to 2002 to chloride concentrations using the weighted average of 5 Greenbrook production wells, the model was run to the year 2041 to assess future implications. Remediation strategies were also investigated via 6 predictive scenarios in which chloride applications were reduced by varying degrees. The results of this phase will be used by the RMOW in cost-benefit analyses of alternative de-icing approaches versus de-chlorination treatment of the well water.

In countries with severe winters such like Canada, winter road maintenance (WRM) operations, such as plowing, salting and sanding, play an indispensible role in maintaining good road surface conditions and keeping roads safe. WRM is, however, also costly, both monetarily and environmentally. The substantial direct and indirect costs associated with WRM have stimulated significant interest in quantifying the safety and mobility benefits of winter road maintenance, such that systematic cost-benefit assessment can be performed. A number of studies have been initiated in the past decade to identify the links between winter road safety and factors related to weather, road, and maintenance operations. However, most of these studies have focused on the effects of adverse weather on road safety. Limited efforts have been devoted to the problem of quantifying the safety benefits of winter road maintenance under specific road weather conditions. Moreover, the joint effects of and complex interactions between road driving conditions, traffic and maintenance and their impact on traffic safety have rarely been studied. This research aims to determine the effect of WRM on road safety during snow storm events and develop models that can be used to quantify the safety benefit of alternative winter road maintenance policies, strategies and practices. Two integral aspects of collision risk were investigated, namely, collision frequency and severity. Collision frequency models were developed using winter storm collision data compiled for six winter seasons (2000 to 2006) for a total of 31 highway routes across Ontario. A comprehensive measure, namely, road surface condition index (RSI), was proposed to represent the road surface conditions during a variety of snow events. RSI was used as a surrogate measure to capture the effects of WRM. Other factors related to weather, traffic and road features were also accounted for in the analysis. Problems associated with data aggregation were also investigated. For this purpose, two different datasets were formed, namely, event-based data (EBD) which aggregates data by snow storm events and hourly based data (HBD) which includes hourly records of collision counts and other related factors. These two data sets of different aggregation levels were then used to investigate the effects of data aggregation and correlation (within – event) as well as to develop models for different purposes of benefit analyses. For EBD, Negative Binomial models and Generalized Negative Binomial models were calibrated whereas for HBD, Generalized Negative Binomial models and multilevel Poisson Lognormal models were calibrated. Generalized Negative Binomial models were found to best fit the data for both datasets. It was found that addition of site specific variables improves model fit. RSI and exposure were found significant for all the models and datasets. Weather factors such as visibility, wind speed, precipitation, and air temperature were also found to have statistically significant effects on collision frequency. All the models were consistent in terms of effects of different variables. The EBD models are useful to quantify the effect of different maintenance service standards and policies with limited information on the details of the weather events and traffic. On the other hand, HBD models have a higher level of reliability capable of providing more accurate estimates on road accidents. As a result, they are useful for determining the effects of different treatment operations. Several examples were employed to demonstrate the application of the developed models, such as quantifying the benefits of alternative maintenance operations and evaluating the effects of different service standards using safety as a performance measure. To enable a comprehensive risk analysis, collisions under both all-weather conditions and snow storm conditions over the six winter seasons were analyzed to identify the relationship between collision severity and various factors related to road weather and surface conditions, road characteristics, traffic, and vehicles etc., on collision severity. A multilevel modeling framework was introduced to capture the inherent hierarchy between collisions, vehicles and persons involved within the collision data. For each collision data set, three alternative severity models, namely, multinomial models, ordered logit models and binary logit models, were calibrated and compared. It was found that multilevel multinomial logit models were best fit to the data. Moreover issues related to different levels of aggregation were also discussed and results from occupant based data were found to be more reasonable and in line with general literature. Different individual, vehicle, environment and accident location factors were found to have a statistically significant effect on the injury severity levels. Contributing factors at the individual and vehicle levels include driver condition, driver sex, driver age, position in vehicle, use of safety device such as seat belt, vehicle type, vehicle age and vehicle condition. Roadway and environment factors include number of lanes, speed limit, road alignment, RSI/road surface condition, wind speed, and visibility. Other factors include light, and traffic volume. Two case studies were conducted to demonstrate the application of the developed models in conjunction with the accident frequency models for cost benefit analysis. This research was the first to investigate the direct link between road surface conditions and collisions at an operational level. It has been shown that the developed models are capable of evaluating alternative winter road maintenance policies and operations and assessing the safety benefit of a particular winter road maintenance strategy or decision. This research is also the first to conduct an in-depth analysis on the problem of winter road safety at a disaggregate level that captures detailed temporal variation (e.g., hourly and by storm event)) within small spatial aggregation units (road sections corresponding to actual patrol routes). The safety models developed from this research could be easily incorporated into a decision support tool for conducting what-if analysis of alternative winter road maintenance policies and methods. Moreover these models could provide a mechanism to estimate road safety level based on road surface as well as weather and traffic conditions and therefore could potentially be used for generating safety related information for travelers as part of a winter traffic management scheme. Directions for future work are also provided at the end of this document.