Freeway merging VISSIM calibration techniques were developed for the analysis of ramp metering in Georgia. An analysis of VISSIM’s advanced merging and cooperative lane change settings was undertaken to determine their effects on merging behavior. Another analysis was performed to determine the effects of the safety reduction factor and the maximum deceleration for cooperative braking parameter on the simulated merging behavior. Results indicated that having both the advanced merging and cooperative lane change setting active produced the best results and that the safety reduction factor had more influence on the merging behavior than the maximum deceleration for cooperative braking parameter. Results also indicated that the on-ramp experienced unrealistic congestion when on-ramp traffic was unable to immediately find an acceptable gap when entering the acceleration lane. These vehicles would form a queue at the end of the acceleration lane and then be unable to merge into the freeway lane due to the speed differential between the freeway and the queued ramp traffic. An Incremental Desired Speed algorithm was developed to maintain an acceptable speed differential between the merging traffic and the freeway traffic. The Incremental Desired Speed algorithm resulted in a smoother merging behavior. Lastly, a ramp meter was introduced and an increase in both the freeway throughput and overall speeds was found. Implications of these findings on the future research is discussed.

Thesis (MDF) -- Stellenbosch University, 2011. / ENGLISH ABSTRACT: The aim of this research report is to analyse the feasibility of Open Road Tolling (ORT) and its development in South Africa through the Gauteng Freeway Improvement Project (GFIP). ORT represents the next generation of Electronic Toll Collection (ETC) and this research report will assess to what extent the GFIP scheme is in line with other comparable tolling schemes; and is the institutional environment amenable to ORT. This will allow one to gauge the feasibility of the scheme and its potential for acceptability and success. The research report outlines the number of risks that come with an ORT scheme and these include amongst others collection risk, enforcement, technology, privacy and public acceptance. The success of the GFIP will largely be determined by how well these risks are mitigated and how the benefits can be marketed to the users. The literature review illustrates that whether road pricing schemes have failed to move forward, have been implemented, are currently under development, or still in the planning stage as a concept there are several consistent lessons and critical success factors one should apply when structuring a scheme. In the discussions with stakeholders, the following conclusions with regards to the feasibility of ORT and its development in South Africa were as follows: - The factors which need to be addressed include political risk, effective marketing of the scheme to the public, obtaining political will and support, building trust between the scheme developer and the user, managing perceptions and acknowledgement of the fact that the scheme will need to prove itself over time. - Inadequate demonstration - Incorporating interoperability yields benefits in terms in terms of network externalities, the ability to use a single transponder for multiple tolling plazas and points, along with the potential for alternative uses for the transponder. - ORT as a viable solution for the GFIP is feasible from a technical point in that it’s the only way in which one can collect tolls from a high volume network and not cause disruptions in the flow of traffic. However, there are a number of persistent residual risks that SANRAL cannot entirely mitigate and some fall under the realm of political risk. - While SANRAL has applied best practice principles in structuring the GFIP with the aim of providing value for money for the user and as far as possible tackling the issue of affordability, there are certain realities, such as the recent global financial crisis, the infrastructure backlog of the country, users paying for roads which were free and challenges with overall service delivery which place a strain on the legitimacy of the GFIP ORT scheme.

The South African government has decided to introduce an extensive toll road system in the Gauteng Province, to fund the road upgrades in the Gauteng Freeway Improvement Project (GFIP). This research report assesses the effectiveness of this funding strategy by analysing the social, economic and environmental impact of the GFIP toll road. The user pay principle is also interrogated to assess the fairness of the toll tariffs to be levied on different user groups. This study has found that the GFIP investment was an unstrategic investment in transport infrastructure. It is proposed that an integrated multi-modal transport strategy is developed, that prioritises the development of the railway system for freight cargo and public transport. As freight vehicles cause more than 99% of roads damage, it is proposed that toll tariffs are only applied to freight vehicles, to lessen the negative social impact of tolling. It is proposed that an independent transport regulator and a consumer council are established, to protect consumer interests, to ensure the independent review of toll tariffs, and to review future public-funded transport investments.

It is widely agreed that estimates of freeway segment travel times are more highly valued by motorists than other forms of traveller information. The provision of real-time estimates of travel times is becoming relatively common in many of the large urban centres in the US and overseas. Presently, most traveler information systems are operating based on estimated travel time rather than predicted travel time. However, traveler information systems are most beneficial when they are built upon predicted traffic information (e.g. predicted travel time). A number of researchers have proposed different models to predict travel time. One of these techniques is based on traffic flow theory and the concept of shockwaves. Most of the past efforts at identifying shockwaves have been focused on performing shockwave analysis based on fixed sensors such as loop detectors which are commonly used in many jurisdictions. However, latest advances in wireless communications have provided an opportunity to obtain vehicle trajectory data that potentially could be used to derive traffic conditions over a wide spatial area. This research proposes a new methodology to detect and analyze shockwaves based on vehicle trajectory data and will use this information to predict travel time for freeway sections. The main idea behind this methodology is that average speed on a section of roadway is constant unless a shockwave is created due to change in flow rate or density of traffic. In the proposed methodology first the road section is discretized into a number of smaller road segments and the average speed of each segment is calculated based on the available information obtained from probe vehicles during the current time interval. If a new shockwave is detected, the average speed of the road segment is adjusted to account for the change in the traffic conditions. In order to detect shockwaves, first, a two phase piecewise linear regression is used to find the points at which a vehicle has changed its speed. Then, the points that correspond to the intersection of shockwaves and trajectories of probe vehicles are identified using a data filtering procedure and a linear clustering algorithm is employed to group different shockwaves. Finally, a linear regression model is applied to find propagation speed and spatial and temporal extent of each shockwave. The performance of this methodology was tested using one simulated signalized intersection, trajectories obtained from video processing of a section of freeway in California, and trajectories obtained from two freeway sections in Ontario. The results of this thesis show that the proposed methodology is able to detect shockwaves and predict travel time even with a small sample of vehicles. These results show that traffic data acquisition systems which are based on anonymously tracking of vehicles are a viable substitution to the tradition traffic data collection systems especially in relatively rural areas.

Includes vita. / Title from PDF of title page. / Document formatted into pages; contains 139 pages. / Thesis (Ph.D.)--University of South Florida, 1998. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Non-recurring congestion caused by incidents is a major source of traffic delay in freeway systems. With the objective of reducing these traffic delays, traffic operation managers are focusing on detecting incident conditions and dispatching emergency management teams to the scene quickly. During the past few decades, a few number of conventional algorithms and artificial neural network models were proposed to automate the process of detecting incident conditions on freeways. These algorithms and models, known as automatic incident detection methods (AIDM), have experienced a varying degree of detection capability. Of these AIDMs, artificial neural network-based approaches have illustrated better detection performance than the conventional approaches such as filtering techniques, decision tree method, and catastrophe theory. So far, a few neural network model structures have been tested to detect freeway incidents. / ABSTRACT: Since the freeway incidents directly affect the freeway traffic flow, majority of these models have used only traffic flow variables as model inputs. However, changes in traffic flow may also be stimulated by the other features (e.g., freeway geometry) to a greater extent. Many AIDMs have also used a conventional detection rate as a performance measure to assess the detection capability. Yet the principle function of incident detection model, which is to identify whether an incident condition exists for a given traffic pattern, is not measured in its entirety by this conventional measure. In this study, new input feature sets, including freeway geometry information, were proposed for freeway incident detection. Sixteen different artificial neural network (ANN) models based on feed forward and recurrent architectures with a variety of input feature sets were developed. ANN models with single and double hidden layers were investigated for incident detection performance. / ABSTRACT: A modified form of a conventional detection rate was introduced to capture full capability of AIDMs in detecting incident patterns in the freeway traffic flow. Results of this study suggest that double hidden layer networks are better than single hidden layer networks. The study has demonstrated the potential of ANNs to improve the reliability using double layer networks when freeway geometric information is included in the model. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

It is widely agreed that estimates of freeway segment travel times are more highly valued by motorists than other forms of traveller information. The provision of real-time estimates of travel times is becoming relatively common in many of the large urban centres in the US and overseas. Presently, most traveler information systems are operating based on estimated travel time rather than predicted travel time. However, traveler information systems are most beneficial when they are built upon predicted traffic information (e.g. predicted travel time). A number of researchers have proposed different models to predict travel time. One of these techniques is based on traffic flow theory and the concept of shockwaves. Most of the past efforts at identifying shockwaves have been focused on performing shockwave analysis based on fixed sensors such as loop detectors which are commonly used in many jurisdictions. However, latest advances in wireless communications have provided an opportunity to obtain vehicle trajectory data that potentially could be used to derive traffic conditions over a wide spatial area. This research proposes a new methodology to detect and analyze shockwaves based on vehicle trajectory data and will use this information to predict travel time for freeway sections. The main idea behind this methodology is that average speed on a section of roadway is constant unless a shockwave is created due to change in flow rate or density of traffic. In the proposed methodology first the road section is discretized into a number of smaller road segments and the average speed of each segment is calculated based on the available information obtained from probe vehicles during the current time interval. If a new shockwave is detected, the average speed of the road segment is adjusted to account for the change in the traffic conditions. In order to detect shockwaves, first, a two phase piecewise linear regression is used to find the points at which a vehicle has changed its speed. Then, the points that correspond to the intersection of shockwaves and trajectories of probe vehicles are identified using a data filtering procedure and a linear clustering algorithm is employed to group different shockwaves. Finally, a linear regression model is applied to find propagation speed and spatial and temporal extent of each shockwave. The performance of this methodology was tested using one simulated signalized intersection, trajectories obtained from video processing of a section of freeway in California, and trajectories obtained from two freeway sections in Ontario. The results of this thesis show that the proposed methodology is able to detect shockwaves and predict travel time even with a small sample of vehicles. These results show that traffic data acquisition systems which are based on anonymously tracking of vehicles are a viable substitution to the tradition traffic data collection systems especially in relatively rural areas.

One of the significant weaknesses of the design speed concept is that it uses the design speed of the most restrictive geometric element as the design speed of the entire road. This leads to potential inconsistencies among successive sections of a road. Previous studies documented that a uniform design speed does not guarantee consistency on rural two-lane facilities. It is therefore reasonable to assume that similar inconsistencies could be found on rural four-lane non-freeway highways. The operating speed-based method is popularly used in other countries for examining design consistency. Numerous studies have been completed on rural two-lane highways for predicting operating speeds. However, little is known for rural four-lane non-freeway highways. This study aims to develop operating speed prediction models for horizontal curves on rural four-lane non-freeway highways using 74 horizontal curves. The data analysis showed that the operating speeds in each direction of travel had no statistical differences. However, the operating speeds on inside and outside lanes were significantly different. On each of the two lanes, the operating speeds at the beginning, middle, and ending points of the curve were statistically the same. The relationships between operating speed and design speed for inside and outside lanes were different. For the inside lane, the operating speed was statistically equal to the design speed. By contrary, for the outside lane, the operating speed was significantly lower than the design speed. However, the relationships between operating speed and posted speed limit for both inside and outside lanes were similar. It was found that the operating speed was higher than the posted speed limit. Two models were developed for predicting operating speed, since the operating speeds on inside and outside lanes were different. For the inside lane, the significant factors are: shoulder type, median type, pavement type, approaching section grade, and curve length. For the outside lane, the factors included shoulder type, median type, approaching section grade, curve length, curve radius and presence of approaching curve. These factors indicate that the curve itself does mainly influence the drivers speed choice.

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This paper presents the development of a novel decentralized freeway merge assistance system using the Dedicated Short Range Communication (DSRC) technology. The system provides visual advisories on a Google map through a smart phone application. To the best of our knowledge, this is the first implementation of a DSRC-based freeway merging assistance system-integrated with smart phone application via Bluetooth-that has been tested in real-world on an interstate highway in an uncontrolled environment. Results from field operational tests indicate that this system can successfully advise drivers towards a collaborative and smooth merging experience on typical "Diamond" interchanges.

Relevance of reactive traffic management strategies such as freeway incident detection has been diminishing with advancements in mobile phone usage and video surveillance technology. On the other hand, capacity to collect, store, and analyze traffic data from underground loop detectors has witnessed enormous growth in the recent past. These two facts together provide us with motivation as well as the means to shift the focus of freeway traffic management toward proactive strategies that would involve anticipating incidents such as crashes. The primary element of proactive traffic management strategy would be model(s) that can separate 'crash prone' conditions from 'normal' traffic conditions in real-time. The aim in this research is to establish relationship(s) between historical crashes of specific types and corresponding loop detector data, which may be used as the basis for classifying real-time traffic conditions into 'normal' or 'crash prone' in the future. In this regard traffic data in this study were also collected for cases which did not lead to crashes (non-crash cases) so that the problem may be set up as a binary classification. A thorough review of the literature suggested that existing real-time crash 'prediction' models (classification or otherwise) are generic in nature, i.e., a single model has been used to identify all crashes (such as rear-end, sideswipe, or angle), even though traffic conditions preceding crashes are known to differ by type of crash. Moreover, a generic model would yield no information about the collision most likely to occur. To be able to analyze different groups of crashes independently, a large database of crashes reported during the 5-year period from 1999 through 2003 on Interstate-4 corridor in Orlando were collected. The 36.25-mile instrumented corridor is equipped with 69 dual loop detector stations in each direction (eastbound and westbound) located approximately every ½ mile. These stations report speed, volume, and occupancy data every 30-seconds from the three through lanes of the corridor. Geometric design parameters for the freeway were also collected and collated with historical crash and corresponding loop detector data. The first group of crashes to be analyzed were the rear-end crashes, which account to about 51% of the total crashes. Based on preliminary explorations of average traffic speeds; rear-end crashes were grouped into two mutually exclusive groups. First, those occurring under extended congestion (referred to as regime 1 traffic conditions) and the other which occurred with relatively free-flow conditions (referred to as regime 2 traffic conditions) prevailing 5-10 minutes before the crash. Simple rules to separate these two groups of rear-end crashes were formulated based on the classification tree methodology. It was found that the first group of rear-end crashes can be attributed to parameters measurable through loop detectors such as the coefficient of variation in speed and average occupancy at stations in the vicinity of crash location. For the second group of rear-end crashes (referred to as regime 2) traffic parameters such as average speed and occupancy at stations downstream of the crash location were significant along with off-line factors such as the time of day and presence of an on-ramp in the downstream direction. It was found that regime 1 traffic conditions make up only about 6% of the traffic conditions on the freeway. Almost half of rear-end crashes occurred under regime 1 traffic regime even with such little exposure. This observation led to the conclusion that freeway locations operating under regime 1 traffic may be flagged for (rear-end) crashes without any further investigation. MLP (multilayer perceptron) and NRBF (normalized radial basis function) neural network architecture were explored to identify regime 2 rear-end crashes. The performance of individual neural network models was improved by hybridizing their outputs. Individual and hybrid PNN (probabilistic neural network) models were also explored along with matched case control logistic regression. The stepwise selection procedure yielded the matched logistic regression model indicating the difference between average speeds upstream and downstream as significant. Even though the model provided good interpretation, its classification accuracy over the validation dataset was far inferior to the hybrid MLP/NRBF and PNN models. Hybrid neural network models along with classification tree model (developed to identify the traffic regimes) were able to identify about 60% of the regime 2 rear-end crashes in addition to all regime 1 rear-end crashes with a reasonable number of positive decisions (warnings). It translates into identification of more than ¾ (77%) of all rear-end crashes. Classification models were then developed for the next most frequent type, i.e., lane change related crashes. Based on preliminary analysis, it was concluded that the location specific characteristics, such as presence of ramps, mile-post location, etc. were not significantly associated with these crashes. Average difference between occupancies of adjacent lanes and average speeds upstream and downstream of the crash location were found significant. The significant variables were then subjected as inputs to MLP and NRBF based classifiers. The best models in each category were hybridized by averaging their respective outputs. The hybrid model significantly improved on the crash identification achieved through individual models and 57% of the crashes in the validation dataset could be identified with 30% warnings. Although the hybrid models in this research were developed with corresponding data for rear-end and lane-change related crashes only, it was observed that about 60% of the historical single vehicle crashes (other than rollovers) could also be identified using these models. The majority of the identified single vehicle crashes, according to the crash reports, were caused due to evasive actions by the drivers in order to avoid another vehicle in front or in the other lane. Vehicle rollover crashes were found to be associated with speeding and curvature of the freeway section; the established relationship, however, was not sufficient to identify occurrence of these crashes in real-time. Based on the results from modeling procedure, a framework for parallel real-time application of these two sets of models (rear-end and lane-change) in the form of a system was proposed. To identify rear-end crashes, the data are first subjected to classification tree based rules to identify traffic regimes. If traffic patterns belong to regime 1, a rear-end crash warning is issued for the location. If the patterns are identified to be regime 2, then they are subjected to hybrid MLP/NRBF model employing traffic data from five surrounding traffic stations. If the model identifies the patterns as crash prone then the location may be flagged for rear-end crash, otherwise final check for a regime 2 rear-end crash is applied on the data through the hybrid PNN model. If data from five stations are not available due to intermittent loop failures, the system is provided with the flexibility to switch to models with more tolerant data requirements (i.e., model using traffic data from only one station or three stations). To assess the risk of a lane-change related crash, if all three lanes at the immediate upstream station are functioning, the hybrid of the two of the best individual neural network models (NRBF with three hidden neurons and MLP with four hidden neurons) is applied to the input data. A warning for a lane-change related crash may be issued based on its output. The proposed strategy is demonstrated over a complete day of loop data in a virtual real-time application. It was shown that the system of models may be used to continuously assess and update the risk for rear-end and lane-change related crashes. The system developed in this research should be perceived as the primary component of proactive traffic management strategy. Output of the system along with the knowledge of variables critically associated with specific types of crashes identified in this research can be used to formulate ways for avoiding impending crashes. However, specific crash prevention strategies e.g., variable speed limit and warnings to the commuters demand separate attention and should be addressed through thorough future research.