Adaptive ramp metering (ARM) is a critical component of smart freeway corridors under an active traffic management portfolio. While improving capacity through smart corridors and application of proactive traffic management solutions is less costly and easier to deploy than freeway widening, conversion to smart corridors still represents a sizable investment for a state department of transportation. Early evidence of improvements following these projects can be valuable to agencies. However, in the U.S. there have been limited evaluations, of smart corridors in general and ARM in particular, based on real operational data. This thesis explores travel time reliability measures for the eastbound (EB) Interstate 80 (I-80) corridor in the San Francisco Bay Area before and after implementation of ARM using INRIX data. These measures include buffer index, planning time, and measures from the literature that account for both skew and width of the travel time distribution. The measures are estimated for the entire corridor as well as corridor segments upstream of a bottleneck that historically have the worst measures of reliability. A new metric for measuring unreliability that may be derived from readily available INRIX data is also proposed in the thesis using data from the study corridor. While the ARM system is relatively new, the results indicate positive trends in measures of reliability even as the number of incidents on the corridor has increased in line with the national crash trends. The spatio-temporal trend evaluation framework used here may be used in the future to obtain more robust conclusions. However, since multiple smart corridor components were installed simultaneously, it may not be possible to fully isolate the effects of the ARM, or any of the other systems, individually.

Recent research at UCF into defining surrogate measures for identifying crash prone conditions on freeways has led to the introduction of several statistical models which can flag such conditions with a good degree of accuracy. Outputs from these models have the potential to be used as real-time safety measures on freeways. They may also act as the basis for the evaluation of several intervention strategies that might help in the mitigation of risk of crashes. Ramp Metering and Variable Speed Limits are two approaches which have the potential of becoming effective implementation strategies for improving the safety conditions on congested freeways. This research evaluates both these strategies in different configurations and attempts to quantify their effect on risk of crash on a 9-mile section of Interstate-4 in the Orlando metropolitan region. The section consists of 17 Loop Detector stations, 11 On-ramps and 10 off-ramps. PARAMICS micro-simulation is used as the tool for modeling the freeway section. The simulated network is calibrated and validated for 5 minute average flows and speeds using loop detector data. Feedback Ramp Metering algorithm, ALINEA, is used for controlling access from up to 7 on-ramps. Variable Speed Limits are implemented based on real-time speed conditions prevailing in the whole 9-mile section. Both these strategies are tested separately as well as collectively to determine the individual effects of all the parameters involved. The results have been used to formulate and recommend the best possible strategy for minimizing the risk of crashes on the corridor. The study concluded that Ramp Metering improves the conditions on the freeway in terms of safety by decreasing variance in speeds and decreasing average occupancy. A safety benefit index was developed for quantifying the reduction in crash risk and it indicated that an optimal implementation strategy might produce benefits of up to 55%. The condition on the freeway section improved with increase in the number of metered ramps. It was also observed that shorter signal cycles for metered ramps were more suitable for metering multiple ramps. Ramp Metering at multiple locations also decreased the segment wide travel-times by 5% and was even able to offset the delays incurred by drivers at the metered on-ramps. Variable Speed Limits (VSL) were individually not as effective as ramp metering but when implemented along with ramp metering, they were found to further improve the safety on the freeway section under consideration. By means of a detailed experimental design it was observed that the best strategy for introducing speed limit changes was to raise the speed limits downstream of the location of interest by 5 mph and not affecting the speed limits upstream. A coordinated strategy - involving simultaneous application of VSL and Ramp Metering - provided safety benefits of up to 56 % for the study section according to the safety benefit index. It also improved the average speeds on the network besides decreasing the overall network travel time by as much as 21%.

Recent research at the University of Central Florida addressing crashes on Interstate-4 in Orlando, Florida has led to the creation of new statistical models capable of calculating the crash risk on the freeway (Abdel-Aty et al., 2004; 2005, Pande and Abdel-Aty, 2006). These models yield the rear-end and lane-change crash risk along the freeway in real-time by using static information at various locations along the freeway as well as real-time traffic data that is obtained from the roadway. Because these models use the real-time traffic data, they are capable of calculating the respective crash risk values as the traffic flow changes along the freeway. The purpose of this study is to examine the potential of two Intelligent Transportation System strategies for reducing the crash risk along the freeway by changing the traffic flow parameters. The two ITS measures that are examined in this research are route diversion and ramp metering. Route diversion serves to change the traffic flow by keeping some vehicles from entering the freeway at one location and diverting them to another location where they may be more efficiently inserted into the freeway traffic stream. Ramp metering alters the traffic flow by delaying vehicles at the freeway on-ramps and only allowing a certain number of vehicles to enter at a time. The two strategies were tested by simulating a 36.25 mile section of the Interstate-4 network in the PARAMICS micro-simulation software. Various implementations of route diversion and ramp metering were then tested to determine not only the effects of each strategy but also how to best apply them to an urban freeway. Route diversion was found to decrease the overall rear-end and lane-change crash risk along the network at free-flow conditions to low levels of congestion. On average, the two crash risk measures were found to be reduced between the location where vehicles were diverted and the location where they were reinserted back into the network. However, a crash migration phenomenon was observed at higher levels of congestion as the crash risk would be greatly increased at the location where vehicles were reinserted back onto the network. Ramp metering in the downtown area was found to be beneficial during heavy congestion. Both coordinated and uncoordinated metering algorithms showed the potential to significantly decrease the crash risk at a network wide level. When the network is loaded with 100 percent of the vehicles the uncoordinated strategy performed the best at reducing the rear-end and lane-change crash risk values. The coordinated strategy was found to perform the best from a safety and operational perspective at moderate levels of congestion. Ramp metering also showed the potential for crash migration so care must be taken when implementing this strategy to ensure that drivers at certain locations are not put at unnecessary risk. When ramp metering is applied to the entire freeway network both the rear-end and lane-change crash risk is decreased further. ALINEA is found to be the best network-wide strategy at the 100 percent loading case while a combination of Zone and ALINEA provides the best safety results at the 90 percent loading case. It should also be noted that both route diversion and ramp metering were found to increase the overall network travel time. However, the best route diversion and ramp metering strategies were selected to ensure that the operational capabilities of the network were not sacrificed in order to increase the safety along the freeway. This was done by setting the maximum allowable travel time increase at 5% for any of the ITS strategies considered.

No description available.

Traffic jams are the cause of approximately 25 billion euro of annual expenses of the state of Germany [14, 26]. In addition to these monetary damages they also have a negative effect on the quality of life for man and nature. They are, for example, the source of a non-negligible part of the 2.4 billion traffic accidents with damage of property or persons registered annually [13]. Circa 14 % of crashes result from previous mishaps [8, 26]. Almost all of them cause further jam formation and therefore a vicious circle which is in danger of closing around. Especially jams being created from spontaneous, non-periodic incidents involve a high risk of ending in a gridlock and thereby disrupt a whole traffic system. Because these disturbances are unpredictable and unpreventable, their duration and impact can merely be reduced by an effective incident management through the systematic, planned and coordinated use of institutional, technical and natural resources. The self-healing-network-strategy by Lämmer [19] and Rausch [27] is one example of such a countermeasure against jam formation in urban road networks. It prevents gridlocks at intersections by means of traffic lights and the combination of two effective mechanisms. Extended off-times prevent the inflow into oversaturated sections and so the expansion of tailbacks onto upstream intersections. Extended green periods of alternative directions of travel motivate drivers to avoid disturbed sectors and thus additionally allow a better exploitation of available free capacities. The development and successful establishment of this method could make the use of ramp metering in urban networks - which is regulating the inflow of oversaturated areas by reducing or skipping green times as well - invalid. To answer this question the presented master’s thesis aims at comparing both inflow-regulating control concepts. However, it is not the ambition to proof that one method is worse than the other, but to contribute to an ideal combination of both instead - with the intention to eliminate deficits on either side. Therefore both concepts will be reviewed in a first step and subsequently the contribution of additional effects against gridlock creation through the symbiosis of the two traffic light controls is analysed. Furthermore, several suggestions for the development of an efficient ramp metering logic are introduced to configure this combination to be more beneficial. / Verkehrsstaus verursachen dem deutschen Staat jährlich rund 25 Milliarden Euro Unkosten [14, 26]. Neben diesen monetären Schäden wirken sie sich jedoch auch negativ auf die Lebensqualität von Mensch und Umwelt aus. Sie sind beispielsweise die Ursache für einen nicht unerheblichen Anteil der circa 2,4 Milliarden jährlich registrierten Unfälle mit Sach- oder Personenschaden [13]. Rund 14 % der Unglücksfälle gehen dabei als Folgeerscheinung eines vorherigen Unfalls hervor [8, 26]. Nahezu alle führen eine weitere Staubildung herbei und somit einen Teufelskreis, der sich zu schließen droht. Besonders aus spontanen, nicht wiederkehrenden Störfällen entstandene Staubildung birgt eine hohes Risiko, in einem Gridlock zu enden und damit ein gesamtes innerstädtisches Verkehrssystem zum Erliegen zu bringen. Da diese Störfälle unvorhersehbar und unvermeidbar sind, kann ihrer Dauer und ihrem Einfluss lediglich ein effizientes Störfallmanagement durch systematischen, gezielten und koordinierten Gebrauch institutioneller, technischer und natürlicher Ressourcen entgegenwirken. Beispiel für eine solche Gegenmaßnahme zur Staubildung in urbanen Netzen ist das selbstorganisierte Störfallmanagement nach Lämmer [19] und Rausch [27]. Mithilfe von LSA und der Kombination zweier effektiver Wirkungsmechanismen werden Gridlocks an Knotenpunkten unterbunden. Verlängerte Sperrzeiten verhindern den Zufluss in übersättigte Streckenabschnitte und damit die Ausbreitung von Rückstaus auf vorgelagerte Kreuzungen. Erweiterte Freigabezeiten der alternativen Fahrtrichtungen regen wiederum eine Umgehung des gestörten Abschnitts an und ermöglichen somit zusätzlich die bessere Ausschöpfung vorhandener freier Kapazitäten. Durch die Entwicklung und den erfolgreichen Einsatz der genannten Maßnahme könnte der Gebrauch von Pförtnerampeln (PA), welche den Zufluss in übersättigte Bereiche ebenfalls regulieren, indem sie Grünzeiten verkürzen oder aussetzen, hinfällig werden. Um diese Frage zu klären, widmet sich die vorliegende Masterarbeit dem Vergleich der beiden zuflussregulierenden Steuerungskonzepte. Ziel ist jedoch nicht, zu beweisen, dass eine Maßnahme schlechter ist als die andere, sondern vielmehr einen Beitrag zu einer optimalen Kombination der Vorzüge beider Herangehensweisen zu leisten und somit die auf beiden Seiten vorhandenen Defizite zu beseitigen. Dafür werden zunächst beide Konzepte auf ihre Effizienz hin überprüft und im Anschluss analysiert, inwiefern die Symbiose beider LSA-Steuerungen in einem Netzwerk zusätzliche Effekte gegen die Gridlockentstehung bewirkt. Außerdem werden verschiedene Vorschläge zur Entwicklung einer effizienteren Steuerlogik für Pförtneranlagen eingebracht, um diese Kombination noch vorteilhafter zu gestalten.

Originally presented as the author's Thesis (doctoral)--Technische Universität München. / "FGV-TUM." Includes bibliographical references (p. 147-156).

Originally presented as the author's Thesis (doctoral)--Technische Universität München. / "FGV-TUM." Includes bibliographical references (p. 147-156).

Adaptive ramp metering (ARM) is a widely popular intelligent transportation system (ITS) tool that boasts the ability to reduce congestion and streamline traffic flow during peak hour periods while maintaining a lower implementation cost than traditional methods such as freeway widening. This thesis explores the effectiveness of ARM implementation on an 18 mile segment of the Interstate 80 (I-80) corridor in the Bay Area residing in northern California. Smaller segments of this particular segment were analyzed to determine the effective length of ARM on efficiency at various lengths originating from a known bottleneck location. Efficiency values were also compared against a control segment of the Interstate 280 (I-280) in San Jose to provide a test site experiencing similar traffic congestion but without any ARM implementation. An Empirical Bayes analysis was conducted to provide the foundation of a safety evaluation of the ramp metering implementation and determine a counterfactual estimate of expected collisions had ARM implementation not occurred. It was found that the installation of the ramp meters did allow for some marginal increases in efficiency but may not be entirely associated with ARM implementation due to a variety of external factors as well as showing inconsistent behavior between analyzed segments. Regarding safety, the predictive model estimates 32.8 collisions to occur along a 0.5 mile segment within a three-year timeframe if ARM were not installed, which implies substantial improvements in safety conditions. However additional efficiency and safety data within the “after” period may be necessary to provide a more robust and conclusive evaluation as the ARM system is still relatively new.

This research evaluates Intelligent Transportation System (ITS) implementation strategies to improve the safety of a freeway once a potential of a crash is detected. Among these strategies are Variable Speed Limit (VSL) and ramp metering. VSL are ITS devices that are commonly used to calm traffic in an attempt to relieve congestion and enhance throughput. With proper use, VSL can be more cost effective than adding more lanes. In addition to maximizing the capacity of a roadway, a different aspect of VSL can be realized by the potential of improving traffic safety. Through the use of multiple microscopic traffic simulations, best practices can be determined, and a final recommendation can be made. Ramp metering is a method to control the amount of traffic flow entering from on-ramps to achieve a better efficiency of the freeway. It can also have a potential benefit in improving the safety of the freeway. This thesis pursues the goal of a best-case implementation of VSL. Two loading scenarios, a fully loaded case (90% of ramp maximums) and an off-peak loading case (60% of ramp maximums), at multiple stations with multiple implementation methods are strategically attempted until a best-case implementation is found. The final recommendation for the off-peak loading is a 15 mph speed reduction for 2 miles upstream and a 15 mph increase in speed for the 2 miles downstream of the detector that shows a high crash potential. The speed change is to be implemented in 5 mph increments every 10 minutes. The recommended case is found to reduce relative crash potential from .065 to -.292, as measured by a high-speed crash prediction algorithm (Abdel-Aty et al. 2005). A possibility of crash migration to downstream and upstream locations was observed, however, the safety and efficiency benefits far outweigh the crash migration potential. No final recommendation is made for the use of VSL in the fully loaded case (low-speed case); however, ramp metering indicated a promising potential for safety improvement.

Ramp metering is an effective management strategy, which helps to keep traffic density below the critical value, preventing breakdowns and thus maintaining the full capacity of the freeway. Warrants for ramp metering installation have been developed by a number of states around the nation. These warrants are generally simple and are based on the traffic, geometry, and safety conditions in the immediate vicinity of each ramp (local conditions). However, advanced applications of ramp metering utilize system-based metering algorithms that involve metering a number of on-ramps to address system bottleneck locations. These algorithms have been proven to perform better compared to local ramp metering algorithms. This has created a disconnection between existing agency metering warrants to install the meters and the subsequent management and operations of the ramp metering. Moreover, the existing local warrants only consider recurrent conditions to justify ramp metering installation with no consideration of the benefits of metering during non-recurrent events such as incidents and adverse weather. This dissertation proposed a methodology to identify the ramps to meter based on system-wide recurrent and non-recurrent traffic conditions. The methodology incorporates the stochastic nature of the demand and capacity and the impacts of incidents and weather using Monte Carlo simulation and a ramp selection procedure based on a linear programming formulation. The results of the Monte Carlo simulation are demand and capacity values that are used as inputs to the linear programming formulation to identify the ramps to be metered for each of the Monte Carlo experiments. This method allows the identification of the minimum number of ramps that need to be metered to keep the flows below capacities on the freeway mainline segment, while keeping the on-ramp queues from spilling back to the upstream arterial street segments. The methodology can be used in conjunction with the existing local warrants to identify the ramps that need to be metered. In addition, it can be used in benefit-cost analyses of ramp metering deployments and associated decisions, such as which ramps to meter and when to activate in real-time. The methodology is extended to address incidents and rainfall events, which result in non-recurrent congestion. For this purpose, the impacts of non-recurrent events on capacity and demand distributions are incorporated in the methodology.