Congestion on the freeways of the U.S. has increased multifold over the past few years. A significant portion of this congestion is caused by non-recurring events such as incidents. Diversion has been accepted as a method that can reduce delays during incidents. The process of diversion involves the selection of the alternate routes, which is currently done off-line and is not responsive to each incident case. The volumes on these preselected routes on that particular day are also ignored. The preselected routes, in most cases, serve only to bypass the link on which the incident occurs. Considering the volumes that flow on the freeways, this leads to considerable delays in terms of lost time and productivity. Another important issue that is currently neglected is user compliance. The network generator is used to reduce the delays in selection of these alternate routes. It uses characteristics such as the congestion levels and available capacities in selection of alternate routes in real-time. Also, used in selecting alternate routes are feasibility criteria, that significantly affect the available capacities on the links. These include presence of trip generators (schools, offices, etc.) or safety factors (icy bridges, height restrictions, etc.). The model thus generates a reduced network and a set of alternate routes to divert the traffic upstream of the incident. Disutilities that drivers associate with route-choice, such as the number of left-turns and signals, the relative time spent on the freeway and arterials are attached to each route. The routes with the minimum disutilities are displayed to the user. A user-equilibrium assignment module to predict traffic flows in the future is also incorporated into the framework. As a precursor to the network generator, there is a module which calculates the clearance time for an incident. It uses other characteristics of the incident such as the weather and time of occurrence in order to predict if the delays are significant to initiate diversion. Numerous tests were conducted in order to validate the rules and functions developed. The tests were based on varying incident and traffic conditions. The results showed that the model, was able to select better routes for off-peak conditions rather than peak conditions. There is a threshold value of the delay caused by the incident, beyond which the model is very effective. / Master of Science

The increasing congestion on the roads in the United States of America has become a topic of much interest among the transportation planners. There is a limit to which there can be building of new highways. Thus, the thrust for better utilization of the existing facilities is gaining ground. This research project is aimed at a solution to problems of increasing travel times due to congestion. A case study of the toll road (Route 44) in the tidewater region of Virginia has been made as an example of the common method of relieving congestion, ridesharing, which is being encouraged. The failure of the proper maintenance of the HOV lanes on the Route 44 leading to the subsequent rescinding of the same has been looked into. The use of DYNAMO programming was made in order to simulate the conditions on the toll road and to get an idea of the various alternatives which could be used to take care of the problem. On the basis of the simulations carried out, and taking into account the views held by the commuters who did not form a part of the ridesharing community, a method in which authorities could give an advantage as far as travel time is concerned, has been proposed. / Master of Science

Accidents are a complex process involving many contributory factors. The understanding of the accident process has often been sought by the use of accident data. Although accident data provide a direct relationship to estimating accident risk, there are many drawbacks associated with the use of these data. The major drawback with the use of accident data is the very fact that traffic engineers have to wait for accidents to occur before any interventions can be made. This alone is significant as the time span required to collect a sample size is often a three-year period. The many deficiencies with accident data have led to alternative measures such as traffic conflict techniques (TCT's) to estimate accident risk.In this investigation. traffic conflict techniques were used to estimate accident risk. There are four basic traffic conflict concepts and the development of these techniques was based on the accident process. The aim of this investigation was to highlight the differences between these concepts and to assess the applicability of these concepts to vehicle-pedestrian conflicts. The investigation was based on applying the various conflict techniques to data obtained at three intersections in the Durban CBD. In order to record the data an innovative method of using digital imaging was employed. This led to the development of a computer program to analyse conflict events. Analysis of the intersections based on the conflict techniques indicates that the intersections of Pine-Field and Commercial-Grey have a high probability of road users being involved in a "serious event" once there is an interaction between them. However, the probability for Commercial-Albert intersection is low thus indicating a safe intersection for vehicle-pedestrian interactions. The number of "serious events" at these locations was found to be related to the interacting traffic volumes - the conflict rate increases with increasing traffic volume. The use of conflict-volume models and accident models together with the conflict concepts agree that the accident risk is related to the conflicting traffic volumes and speed of the road users. / Thesis (M.Sc.Eng.)-University of Natal, Durban, 2003.

Both traffic congestion and road accidents impose a burden on society, and it is therefore important for transport policy makers to reduce their impact. An ideal scenario would be that traffic congestion and accidents are reduced simultaneously, however, this may not be possible since it has been speculated that increased traffic congestion may be beneficial in terms of road safety. This is based on the premise that there would be fewer fatal accidents and the accidents that occurred would tend to be less severe due to the low average speed when congestion is present. If this is confirmed then it poses a potential dilemma for transport policy makers: the benefit of reducing congestion might be off-set by more severe accidents. It is therefore important to fully understand the relationship between traffic congestion and road accidents while controlling for other factors affecting road traffic accidents. The relationship between traffic congestion and road accidents appears to be an under researched area. Previous studies often lack a suitable congestion measurement and an appropriate econometric model using real-world data. This thesis aims to explore the relationship between traffic congestion and road accidents by using an econometric and GIS approach. The analysis is based on the data from the M25 motorway and its surrounding major roads for the period 2003-2007. A series of econometric models have been employed to investigate the effect of traffic congestion on both accident frequency (such as classical Negative Binomial and Bayesian spatial models) and accident severity (such as ordered logit and mixed logit models). The Bayesian spatial model and the mixed logit model are the best models estimated for accident frequency and accident severity analyses respectively. The model estimation results suggest that traffic congestion is positively associated with the frequency of fatal and serious injury accidents and negatively (i.e. inversely) associated with the severity of accidents that have occurred. Traffic congestion is found to have little impact on the frequency of slight injury accidents. Other contributing factors have also been controlled for and produced results consistent with previous studies. It is concluded that traffic congestion overall has a negative impact on road safety. This may be partially due to higher speed variance among vehicles within and between lanes and erratic driving behaviour in the presence of congestion. The results indicate that mobility and safety can be improved simultaneously, and therefore there is significant additional benefit of reducing traffic congestion in terms of road safety. Several policy implications have been identified in order to optimise the traffic flow and improve driving behaviour, which would be beneficial to both congestion and accident reduction. This includes: reinforcing electronic warning signs and the Active Traffic Management, enforcing average speed on a stretch of a roadway and introducing minimum speed limits in the UK. This thesis contributes to knowledge in terms of the relationship between traffic congestion and road accidents, showing that mobility and safety can be improved simultaneously. A new hypothesis is proposed that traffic congestion on major roads may increase the occurrence of serious injury accidents. This thesis also proposes a new map-matching technique so as to assign accidents to the correct road segments, and shows how a two-stage modelling process which combines both accident frequency and severity models can be used in site ranking with the objective of identifying hazardous accident hotspots for further safety examination and treatment.

Submitted in fulfillment for the degree of Magister Technologiae: Engineering: Civil, Durban University of Technology, Durban, South Africa, 2013. / An increase in road traffic, poor road conditions and high numbers of road accidents are major challenges at the Port of Durban. Roads are considered as the most important transport mode at the Port of Durban. Road transport has taken almost 80% of the import and export cargo while railway transport is left with approximately 20%. It is estimated that 75 million lives in the world will be lost and 750 million people injured in road accidents in the first half of the 21th century. The Port of Durban is an important vehicle for facilitating economic growth of local, regional and national industries. For the Port to maintain global competitiveness with the current trend of globalization, it has to ensure that roads are well maintained, safe and have a smooth traffic flow with no delays. This study provides an overview of the road infrastructure within the Port of Durban in relation to road condition, safety, law enforcement and traffic. Existing and ongoing studies conducted in South Africa and abroad form part of the literature review. This study identifies factors that are affecting the condition of road infrastructure such as growth of container cargo, an increase in the dimension and weight of trucks, transport deregulation, port layout and handling equipments. Deregulation of road transport over the past years has resulted in an 80:20 split between road and rail transport putting more pressure on roads. Cost and time are the major deciding factors in the freight industry. Most customers prefer to use road transport due to the lower cost and reduced time compared to rail transport. There are eight major roads within the Port that connect the South, West and North of eThekwini Municipality namely Bayhead, Quayside, Maydon, Rick Turner, Wisely, South Coast, Bluff and Iran Roads. Asset verification and assessment of the condition of the existing eight major roads found that Quayside Road is in a better condition compared to the other roads. Maydon and South Coast Roads are low rated roads which are in a poor condition. Identification and assessment of the condition of 210 000m² of asphalt paved areas which included minor roads within the Port of Durban was also conducted. Most paved areas and roads fall under D (fair) category which is reasonable but maintenance work may be required within six months. Comparison between the condition of the eight major roads within the Port and outside the Port was investigated. The findings indicate that sections of roads outside the Port are in a better condition than sections within the Port. Traffic counts were conducted in order to determine the utilization of the existing eight major roads. Bayhead and South Coast Road are highly utilized roads. Road accident reports and death reports were analyzed on these roads. Most of the road accidents take place on South Coast Road. A questionnaire survey was conducted, targeting road users who travel on these roads within the Port. Feedback was obtained on the status of road conditions, safety and traffic within the Port of Durban. Findings of this survey revealed that most of the respondents don’t know where to report road defect/s within the Port. Approximately 37.5% of the road users felt not safe to drive on roads within the Port especially on South Coast Road. A high percentage of people (93%) witnessed accidents on these roads. Traffic signals within the Port are maintained by eThekwini Municipality and are very often non-functional. When road signs need to be repaired or replaced, it takes longer than expected. Also, there are limited parking areas around the Port resulting in trucks parking closer to the premises while waiting to collect or deliver cargo. This causes major traffic congestion, for example, on Maydon Road where most trucks park on the side of the road. Recommendations include assessment guidelines that could improve road condition, safety and traffic flow. Areas to be improved with regard to road infrastructure are also highlighted. / M

With the remarkable increase in the population and number of vehicles, traffic has become a severe problem in most metropolitan areas. Traffic congestion has imposed tight constraints on economic growth, national security, and mobility of riders and goods. The open-mode integrated transportation system (OMITS) has been designed to improve the traffic condition of roadways by increasing the ridership of vehicles and optimizing transportation modes through smart services integrating emerging information communication technologies, big data management, social networking, and transportation management. Even a modest reduction in the number of vehicles on roadways will lead to a considerable cost savings in terms of time and money. Additionally the reduction in traffic jams will lead to a significant decrease in both gasoline consumption and greenhouse gas emissions. As a result, novel transportation management is critical to reduce vehicle mileage in the peak time of the road network. The OMITS was proposed to enhance transportation services in respect to the following three aspects: optimization of the transportation modes by multimodal traveling assignment, dynamic routing and ridesharing service with advanced traveler information systems, and interactive user interface for social networking and traveling information. Therefore, the OMITS encompasses a broad range of advanced transportation research topics, say dynamic trip- match, transportation-mode optimization, traffic prediction, dynamic routing, and social network- based carpooling. This dissertation will focus on a kernel part of the OMITS, namely traffic simulation and prediction based on data containing the distribution of vehicles and the road network configuration. A microscopic traffic simulation framework has been developed to take into account various traffic phenomena, such as traffic jams resulting from bottlenecking, incidents, and traffic flow shock waves. Four fundamental contributions of the present study are summarized as follows: Firstly, an accurate and robust vehicle trajectory data collection method based on image data of unmanned aerial vehicle (UAV) has been presented, which can be used to rapidly and accurately acquire the real-time traffic conditions of the region of interest. Historically, a lack in the availability of trajectory data has posed a significant obstacle to the enhancement of microscopic simulation models. To overcome this obstacle, a UAV based vehicle trajectory data collection algorithm has been developed. This method extracts vehicle trajectory data from the UAV’s video at different altitudes with different view scopes. Compared with traditional methods, the present data collection algorithm incorporates many unique features to customize the vehicle and traffic flow, through which vehicle detection and tracking system accuracy can be considerably increased. Secondly, an open mechanics-based acceleration model has been presented to simulate the longitudinal motion of vehicles, in which five general factors—namely the subject vehicle’s speed and acceleration sensitivity, safety consideration, relative speed sensitivity and gap reducing desire—have been identified to describe drivers’ preferences and the interactions between vehicles. Inspired by the similarity between vehicle interactions and particle interactions, a mechanical system with force elements has been introduced to quantify the vehicle’s acceleration. Accordingly, each of the aforementioned five factors are assumed to function as an individual trigger to alter each vehicle’s speed. Based on Newton’s second law of motion, the subject vehicle’s longitudinal behavior can be simulated by the present open mechanics-based acceleration model. By introducing feeling gap, multilane acceleration behavior is included in the presented model. The simulation results fit realistic conditions for the traffic flow and the road capacity very well, where traffic shockwaves can be observed for a certain range of the traffic density. This model can be extended to more general scenarios if other factors can be recognized and introduced into the modeling framework. Thirdly, a driver decision-based lane change execution model has been developed to describe a vehicle’s lane change execution process, which includes two steps, i.e. driver’s lane selection and lane change execution. Currently, most lane change models focus on the driver’s lane selection, and overlook the driver’s behavior during a process of lane change execution which plays a significant role in the simulation of traffic flow characteristics. In this model, a lane change execution is analyzed as a driver’s decision-making process, which consists of desire point setting, priority decision-making, corresponding actions and achievement of consensus analysis. Compared with the traditional lane change execution models, the present model describes a realistic lane change process, and it provides more accurate and detailed simulation results in the microscopic traffic simulation. Based on the presented open mechanics-based acceleration model and the driver decision- based lane change execution model, a reverse lane change model has further been developed to simulate some complex traffic situations such as reverse lane change process at a two-way-two- lane road section where one lane is blocked by a traffic incident. Based on this reverse lane change model, information on the average waiting time and road capability can be obtained. The simulation results show that the present model is able to reflect real driver behavior and the corresponding traffic phenomenon during a reverse lane change process Through a homogenization process of the microscopic vehicle motion, we can obtain the macroscopic traffic flow of the roadway network within certain time and spatial ranges, which will be integrated into the OMITS system for traffic prediction. The validation of the models through future OMITS operations will also enable them to be high fidelity models in future driverless technologies and autonomous vehicles.

Thesis (Ph.D. (Town and Regional Planning))--North-West University, Potchefstroom Campus, 2008.

In current study, a mechanism to extract traffic related information such as congestion and incidents from textual data from the internet is proposed. The current source of data is Twitter, however, the same mechanism can be extended to any kind of text available on the internet. As the data being considered is extremely large in size automated models are developed to stream, download, and mine the data in real-time. Furthermore, if any tweet has traffic related information then the models should be able to infer and extract this data. To pursue this task, Artificial Intelligence, Machine Learning, and Natural Language Processing techniques are used. These models are designed in such a way that they are able to detect the traffic congestion and traffic incidents from the Twitter stream at any location. Currently, the data is collected only for United States. The data is collected for 85 days (50 complete and 35 partial) randomly sampled over the span of five months (September, 2014 to February, 2015) and a total of 120,000 geo-tagged traffic related tweets are extracted, while six million geo-tagged non-traffic related tweets are retrieved. The classification models for detection of traffic congestion and incidents are trained on this dataset. Furthermore, this data is also used for various kinds of spatial and temporal analysis. A mechanism to calculate level of traffic congestion, safety, and traffic perception for cities in U.S. is proposed. Traffic congestion and safety rankings for the various urban areas are obtained and then they are statistically validated with existing widely adopted rankings. Traffic perception depicts the attitude and perception of people towards the traffic. It is also seen that traffic related data when visualized spatially and temporally provides the same pattern as the actual traffic flows for various urban areas. When visualized at the city level, it is clearly visible that the flow of tweets is similar to flow of vehicles and that the traffic related tweets are representative of traffic within the cities. With all the findings in current study, it is shown that significant amount of traffic related information can be extracted from Twitter and other sources on internet. Furthermore, Twitter and these data sources are freely available and are not bound by spatial and temporal limitations. That is, wherever there is a user there is a potential for data.

Much has been written about the desperate need to seek alternative solutions to the urban traffic congestion problems we face today. To manage these problems, three main streams of intervention have evolved from supply, demand and land-use management paradigms. Whilst their underlying measures all have the ability to reduce traffic congestion one way or another, little has been done to integrate the measures of each stream using a qualitative decision framework or process enabling the selection of site specific measures appropriate to local traffic and transport conditions. To this end the study reports the results of an empirical investigation by which a Multi-Criteria Analysis based Dynamic Adaptive Decision Framework (D.A.D.F.) were developed. This screens selected measures identifying those that have the potential to alleviate site specific road based traffic congestion. The product of the screening process is a set of sustainable measures transposed into an integrated strategy tailored to address local traffic congestion issues. Once the D.A.D.F. was developed, its usefulness and workability was tested by applying it to a case study. The case study results demonstrated that the D.A.D.F. is capable of producing integrated strategies with the ability to manage traffic congestion, encourage sustainable development and alleviate some site specific development challenges within the context of the study areas. Going beyond developing the decision framework, the study recommends positioning the D.A.D.F. within the South African planning system as part of a Traffic Congestion Management Plan (T.C.M.P.), setting out its likely components within the local context. It is concluded that the main innovation of the study is the development of the D.A.D.F., the T.C.M.P., the recommendations to situate both in the planning system and the comprehensive, but still transparent, approach undertaken to create integrated strategies for specific local conditions consisting of elements that work together to produce cumulative short to long term effects that attain a balanced set of environmental, social and economic goals - all imperative for sustainable development. / Thesis (Ph.D. (Town and Regional Planning))--North-West University, Potchefstroom Campus, 2008.

Much has been written about the desperate need to seek alternative solutions to the urban traffic congestion problems we face today. To manage these problems, three main streams of intervention have evolved from supply, demand and land-use management paradigms. Whilst their underlying measures all have the ability to reduce traffic congestion one way or another, little has been done to integrate the measures of each stream using a qualitative decision framework or process enabling the selection of site specific measures appropriate to local traffic and transport conditions. To this end the study reports the results of an empirical investigation by which a Multi-Criteria Analysis based Dynamic Adaptive Decision Framework (D.A.D.F.) were developed. This screens selected measures identifying those that have the potential to alleviate site specific road based traffic congestion. The product of the screening process is a set of sustainable measures transposed into an integrated strategy tailored to address local traffic congestion issues. Once the D.A.D.F. was developed, its usefulness and workability was tested by applying it to a case study. The case study results demonstrated that the D.A.D.F. is capable of producing integrated strategies with the ability to manage traffic congestion, encourage sustainable development and alleviate some site specific development challenges within the context of the study areas. Going beyond developing the decision framework, the study recommends positioning the D.A.D.F. within the South African planning system as part of a Traffic Congestion Management Plan (T.C.M.P.), setting out its likely components within the local context. It is concluded that the main innovation of the study is the development of the D.A.D.F., the T.C.M.P., the recommendations to situate both in the planning system and the comprehensive, but still transparent, approach undertaken to create integrated strategies for specific local conditions consisting of elements that work together to produce cumulative short to long term effects that attain a balanced set of environmental, social and economic goals - all imperative for sustainable development. / Thesis (Ph.D. (Town and Regional Planning))--North-West University, Potchefstroom Campus, 2008.