The focus of this project is highway construction zones and the impact of lane closures on traffic. By measuring throughput (the number of vehicles that can flow through a work zone in a given amount of time), analysis was performed to determine the factors that affect this value. Successful data collection uncovered a paradox. Often work zones would see no queuing, indicating the lane could have been closed for longer hours. Alternatively sites with forced flow allowed fewer vehicles through the site than expected, indicating that the design value is too high, requiring shorter lane closure hours. A generic model for throughput was developed as well as a better functioning highway specific model. In addition to these models, this project developed a spreadsheet-based form of analysis named SZUDA (Simplified work Zone User Delay Analysis) that determines queue length and approximate costs associated with road user delay.

No description available.

Adverse weather conditions can increase travel risk. Understanding how drivers react to adverse weather, such as rainfall, can aid in the understanding of road safety patterns and traffic operations. This information can in turn be used to improve driver education as well as highway operation through improved signing or the introduction of intelligent highway systems. Hourly rainfall data collected from the Pearson International Airport weather station and City of Toronto traffic data collected at the study site on the Gardiner expressway were used to create event and control pairs. In total, 115 hours with rainfall were matched to control data one week before or after the rainfall event. The traffic sensor at the study site collected speed, volume, and occupancy data at 20-second intervals, which was aggregated to five minutes. In addition, speed deviation and headway data at the 5-minute interval were used for analysis purposes. Two methods were used to test the effects of rainfall on traffic variables and the relationships between them. Matched pair t-tests were used to determine the magnitude of change between event and control conditions for the volume, speed, speed deviation, and headway variables for congested and uncongested traffic conditions. In addition, stepwise multiple linear regression was used to test the effects of rainfall on speed-volume and volume-occupancy relationships. Results of the matched pair t-tests indicated that volumes, speeds, and speed deviations dropped in event conditions, while headways increased slightly. Changes tended to be greater for congested than uncongested conditions. Linear regression results indicated that changes in speed were sensitive to volume conditions, and changes in volume were sensitive to occupancy, although only to a limited extent. Overall, drivers respond to rainfall conditions by reducing both speed and speed deviations, and increasing headway. Reductions in speed are larger in congested conditions, while increases in headway are smaller. Taken in combination, drivers are taking positive steps in order to either maintain or improve safety levels.

A good understanding of the relationship between highway performance, such as crash rates and travel delays, and winter road maintenance activities under different winter weather and traffic conditions is essential to the development of cost-effective winter road maintenance policies and standards, operation strategies and technologies. This research is specifically concerned about the mobility benefit of winter road maintenance. A microscopic traffic simulation model is used to investigate the traffic patterns under adverse weather and road surface conditions. A segment of the Queen Elizabeth Way (QEW) located in the Great Toronto Area, Ontario is used in the simulation study. Observed field traffic data from the study segment was used in the calibration of the simulation model. Different scenarios of traffic characteristics and road surface conditions as a result of weather events and maintenance operations are simulated and travel time is used as a performance measure for quantifying the effects of winter snow storms on the mobility of a highway section. The modeling results indicate that winter road maintenance aimed at achieving bare pavement conditions during heavy snowfall could reduce the total delay by 5 to 36 percent, depending on the level of congestion of the highway. The simulation results are then applied in a case study for assessing two maintenance policy decisions at a maintenance route level.

Road traffic safety has increasingly become in need of educated road safety professionals, as the number of accidents in the World Health Organization member countries exceeds one million. The profession itself is transitioning from experience based decision making to empirical, theoretical and mathematical based solutions. However, road traffic safety is a multidiscipline, crossing over many fields and requiring a high degree of communication between different institutions. There are very few institutions that provide programs in the field; furthermore, they employ traditional lecture-based teaching methods. The traditional teaching environment does not fulfill the educational needs of future traffic safety professionals due to its rigidity and lack of problem solving exercises. An alternative method, namely problem based learning, is recommended as an alternative teaching method in this paper. The thesis is constructed in such a way as to develop a complete road traffic safety educational module at graduate and post graduate level. The theoretical basis on which a road traffic safety module is later built is presented in the first part of the thesis. Major concepts in road traffic safety, as well as problem based learning methods are investigated. In addition, a literature review SWOT analysis based on literature is conducted.The module development consists of establishing the road traffic safety learning goals for each segment in the module, appropriate assessment criteria and group work format. The module contains gradual difficulty level problems, starting from the easiest topic and easiest format (closed ended problem) and ending with the hardest topic and hardest format (open ended problem). The last section employs the SWOT analysis findings in the theoretical section to develop a SWOT analysis of the road traffic safety module presented in the thesis.

Purpose- The purpose of this research is to explore the role of road transport in logistics operations, and to investigate and analyze how IKEA China does operate on road transport in logistics operations. Design/methodology/approach- A single case study has been conducted at IKEA China, including semi-structured interviews and review of internal documents. Along with the case study, literature reviews have been conducted within the areas of efficient road transport in logistics operations.   Findings- The IKEA China case suggests that the logistics operations should have strong link to the efficient road transport in a manner optimized logistics operations can provide efficient road transport with less cost.   Research limitations/implications- This thesis is limited to one representative company, and the authors just focus on a study of efficient road transport in logistics operations for narrowing down the thesis. So the solutions and proposals about efficient road transport might not be adopted by other companies or be applied to other parts of the supply chain. Additionally, a study of efficient road transport can be discussed, analyzed and studied from a lot of different perspectives, even much better in a holistic viewpoint. Here, the authors just choose a few primary perspectives as research objectives to support this study, which concerns the data and information collected from IKEA China. Finally, because of the limitation of time and personal knowledge, the data collected from IKEA China may neither abundant enough nor deep enough in a manner without exploiting and expanding into all the issues and challenges refers to efficient road transport in logistics operations.   Practical implication- This research provides suitable solutions for a company towards efficient road transport in logistics operations. Consequently, it will facilitate companies to achieve the purpose of efficient road transport by optimizing their logistics operations in a manner improve the outcomes of insouring/outsourcing, merge-in-transit, consolidation point and packaging.   Originality/value- This research combines five theoretical fields in terms of sourcing strategy, merge-in-transit, consolidation point, packaging as well as economics scale of trucking cost to contribute proposals to efficient road transport in logistics operations.   Keywords Efficient road transport, Logistics operations, cost

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Road conditions are a critical issue for road users as, if not given sufficient attention, they may threaten users’ lives. The environmental parameters, such as snowy, icy, dry and wet, are important in relation to the condition of roads. This is particularly true in relation to the northern regions and greatest concern should be in relation to snowy and icy situations. In this thesis, a system based on an InGaAs area scan sensor utilizes NIR technology to detect water or ice on the road so as to enable drivers to avoid slippery road conditions. The conditions caused by freezing water on road surface are particularly dangerous and are not easy to observe and it is hope that this project will boost traffic safety. The system is able to assist road maintenance personnel in forecasting and detecting slippery road conditions during winter road maintenance (WRM). The system, which is based on FPGA, has functionalities that display the captured images on an HDMI monitor and send the images to the software on a host PC via the UART protocol. An interface board, which carries the sensor and which connects to the FPGA board, is developed for NIR sensor. VHDL implementation and PC software design are the works included in the project. Besides, this device is exploited utilizing InGaAs image sensor. According to its features, it can be applied in other applications which will also be discussed. Finally, experiments are conducted in order to investigate the system’s operation with the variation of temperature.

Adverse weather conditions can increase travel risk. Understanding how drivers react to adverse weather, such as rainfall, can aid in the understanding of road safety patterns and traffic operations. This information can in turn be used to improve driver education as well as highway operation through improved signing or the introduction of intelligent highway systems. Hourly rainfall data collected from the Pearson International Airport weather station and City of Toronto traffic data collected at the study site on the Gardiner expressway were used to create event and control pairs. In total, 115 hours with rainfall were matched to control data one week before or after the rainfall event. The traffic sensor at the study site collected speed, volume, and occupancy data at 20-second intervals, which was aggregated to five minutes. In addition, speed deviation and headway data at the 5-minute interval were used for analysis purposes. Two methods were used to test the effects of rainfall on traffic variables and the relationships between them. Matched pair t-tests were used to determine the magnitude of change between event and control conditions for the volume, speed, speed deviation, and headway variables for congested and uncongested traffic conditions. In addition, stepwise multiple linear regression was used to test the effects of rainfall on speed-volume and volume-occupancy relationships. Results of the matched pair t-tests indicated that volumes, speeds, and speed deviations dropped in event conditions, while headways increased slightly. Changes tended to be greater for congested than uncongested conditions. Linear regression results indicated that changes in speed were sensitive to volume conditions, and changes in volume were sensitive to occupancy, although only to a limited extent. Overall, drivers respond to rainfall conditions by reducing both speed and speed deviations, and increasing headway. Reductions in speed are larger in congested conditions, while increases in headway are smaller. Taken in combination, drivers are taking positive steps in order to either maintain or improve safety levels.

A good understanding of the relationship between highway performance, such as crash rates and travel delays, and winter road maintenance activities under different winter weather and traffic conditions is essential to the development of cost-effective winter road maintenance policies and standards, operation strategies and technologies. This research is specifically concerned about the mobility benefit of winter road maintenance. A microscopic traffic simulation model is used to investigate the traffic patterns under adverse weather and road surface conditions. A segment of the Queen Elizabeth Way (QEW) located in the Great Toronto Area, Ontario is used in the simulation study. Observed field traffic data from the study segment was used in the calibration of the simulation model. Different scenarios of traffic characteristics and road surface conditions as a result of weather events and maintenance operations are simulated and travel time is used as a performance measure for quantifying the effects of winter snow storms on the mobility of a highway section. The modeling results indicate that winter road maintenance aimed at achieving bare pavement conditions during heavy snowfall could reduce the total delay by 5 to 36 percent, depending on the level of congestion of the highway. The simulation results are then applied in a case study for assessing two maintenance policy decisions at a maintenance route level.