Browsing by Subject "Freeways"
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Item Development and Demonstration of Merge Assist System using Connected Vehicle Technology(Center for Transportation Studies, University of Minnesota, 2019-04) Hussain, Shah; Peng, Zhiyuan; Hayee, M. ImranOne potential area to improve driver safety and traffic mobility is around merge points of two roadways, e.g., at a typical freeway entrance ramp. Due to poor visibility because of weather or complex road infrastructure, on many such entrance ramps, it may become difficult for the driver on the merging/entrance ramp to clearly see the vehicles travelling on the main freeway, making it difficult to merge. A fundamental requirement to facilitate many advance driver assistance systems (ADAS) functions including a merge assist system is to accurately acquire vehicle positioning information. Accurate position information can be obtained using either sensor-based systems (camera-based, radar, lidar) or global navigation satellite systems (GPS, DGPS, RTK). For these systems to work well for practical road and weather conditions, advanced techniques and algorithms are needed, which make the system complex and expensive to implement. In this research project, we propose a merge assist system by acquiring the relative positioning of vehicles using standard GPS receivers and dedicated short-range communication (DSRC) based vehicle-to-vehicle (V2V) communication. The DSRC-equipped vehicles travelling on the main freeway and on the entrance-ramp will periodically communicate their positioning information with each other. Using that information, the relative trajectories, relative lane, and position of all DSRC-equipped vehicles travelling on the main freeway will be calculated and recorded in real time in the vehicle travelling on the entrance ramp. Finally, a merge-time cushion will also be calculated, which could potentially be used to assist the driver of the ramp vehicle to safely merge into the freeway.Item Development of the Next Generation Stratified Ramp Metering Algorithm Based on Freeway Density(Center for Transportation Studies, 2011-03) Geroliminis, Nikolas; Srivastava, Anupam; Michalopoulos, PanosA new coordinated, traffic-responsive ramp metering algorithm has been designed for Minnesota’s freeways based on density measurements, rather than flows. This is motivated in view of recent research indicating that the critical value of density at which capacity is observed is less sensitive and more stable than the value of capacity, thereby resulting in m ore effective control. Firstly, we develop a methodology to estimate densities with space and time based on data from loop detectors. The methodology is based on solving a flow conservation differential equation (using LWR theory) with intermediate (internal) freeway mainline boundaries, which is fast er and more accurate from previous resear ch using only external boundaries. To capture the capacity drop phenomenon into the first-order model we utilize a fundamental diagram with two values of capacity and we provide a memory-based methodology to choose the appropriate value in the numerical solution of the problem. Secondly, with respect to ramp metering, the main goals of the algorithm are to delay the onset of the breakdown and to accelerate system recovery when ramp metering is unable due to the violation of maximum allowable ramp waiting time. The effectiveness of the new control strategy is being assessed by comparison with the currently deployed version of the Stratified Zone Algorithm (SZM) through microscopic simulation of a real 12-mile, 17 ramp freeway section. Simulations show a decrease in the delays of mainline and ramp traffic, an improvement 8% in the overall delays and avoidance of the maximum ramp delay violations.Item Dynamic Estimation of Origin-Destination Patterns in Freeways(Minnesota Department of Transportation, 1994-05) Davis, Gary A.Any proposed traffic management action is essentially a forecast that the action will result in certain traffic conditions, but uncertainty concerning the amount and distribution of traffic demand will introduce random error between what is expected and what actually occurs. This report treats the problem of forecasting whether or not a given set of freeway on-ramp volumes are likely to cause over-capacity demand at some point in the freeway mainline. The main source of uncertainty in these forecasts concerns the freeway's origin-destination matrix, and four different methods for estimating this matrix from loop detector data are evaluated using Monte Carlo simulation. Only the method which explicitly modeled freeway traffic flow produced reasonably unbiased and efficient estimates, and it was concluded that successful estimation must be coupled with a good model of freeway traffic flow.Item Efficiency and equity of orbital motorways in Madrid(Journal of Transport and Land Use, 2010) Martín, Juan Carlos; García-Palomares, Juan Carlos; Gutiérrez, Javier; Román, ConcepciónOrbital motorways are major structuring elements in the metropolitan areas of developed countries. They can be considered as key components within the transport network of large urban agglomerations, funneling a great amount of intra- and inter metropolitan traffic. This paper explores the equity and efficiency effects of orbital motorways on accessibility, using the beltways of Madrid as a case study. It is well known that orbital impacts differ depending on their location within the metropolitan area (inner and outer) as well as the activity distributional performance (agglomeration vs. decentralization of activities). These topics have received very little attention in previous studies. The paper extracts some policy considerations with respect to accessibility disparities within metropolitan areas and com- pares relative changes from the spatial perspective.Item Estimation of Metro Freeway System Reliability and Resilience(Minnesota Department of Transportation, 2022-02) Kwon, Eil; Jurrens, Chet; Wright, Cole; Mahmud, AsifThis study has estimated and analyzed the travel-time reliability and traffic-flow performance trends of the freeway corridors in the Twin Cities metro area of Minnesota. First, TeTRES (Travel-Time Reliability Estimation System), developed in the previous study, was enhanced by adding the estimation module of the traffic-flow performance measures for selected routes. Next, the TeTRES database was populated with the external-operating condition data collected from 2010 to 2020. The enhanced TeTRES was then applied to a total of 48 directional corridors in the metro freeway network and the travel-time reliability for each corridor under different operating conditions was estimated and analyzed along with the traffic-flow performance measures for 2016-2020 period. In particular, a newly developed vulnerability index, which combines 95th percentile buffer index and 95th percentile travel rate of each route, was applied to determine yearly-reliability trends under different operating conditions for each corridor. The vulnerability index was also applied to identify the most vulnerable bottleneck section within each directional corridor using the 2019 data under all conditions. Finally, a preliminary study to assess the operational resilience of freeway corridors was conducted in this study by formulating the corridor-wide operational resilience with data from a total of six directional corridor routes in the metro freeway network.Item Freeway Network Traffic Detection and Monitoring Incidents(Minnesota Department of Transportation, 2007-10) Joshi, Ajay J.; Atev, Stefan; Fehr, Duc; Drenner, Andrew; Bodor, Robert; Masoud, Osama; Papanikolopoulos, Nikolaos P.We propose methods to distinguish between moving cast shadows and moving foreground objects in video sequences. Shadow detection is an important part of any surveillance system as it makes object shape recovery possible, as well as improves accuracy of other statistics collection systems. As most such systems assume video frames without shadows, shadows must be dealt with beforehand. We propose a multi-level shadow identification scheme that is generally applicable without restrictions on the number of light sources, illumination conditions, surface orientations, and object sizes. In the first level, we use a background segmentation technique to identify foreground regions that include moving shadows. In the second step, pixel-based decisions are made by comparing the current frame with the background model to distinguish between shadows and actual foreground. In the third step, this result is improved using blob-level reasoning that works on geometric constraints of identified shadow and foreground blobs. Results on various sequences under different illumination conditions show the success of the proposed approach. Second, we propose methods for physical placement of cameras in a site so as to make the most of the number of cameras available.Item Real-Time Traffic Prediction for Advanced Traffic Management Systems: Phase I(Intelligent Transportation Systems Institute, University of Minnesota, 1995-10) Davis, Gary A.; Stephanedes, Yorgos J.; Kang, Jeong-GyuIt has been recommended that Advanced Traffic Management Systems (ATMS) must work in real-time, must respond to and predict changes in traffic conditions, and must included areawide detection surveillance. To support such ATMS, this project developed a tractable, stochastic model of freeway traffic flow and travel demand which satisfies three primary objectives. First, the model should generate real-time estimates of traffic state variables from loop detector data, which can in turn be used as time-varying initial conditions for more comprehensive simulation models, such as KRONOS or FREESIM. Second, the model should generate its own predictions of mainline and off-ramp traffic volumes, as well as calculate the expected error associated with these predictions, thus supporting the use of both deterministic and stochastic optimization for determining traffic management actions. Third, the model should be capable of full on-line implementation, in that it should be capable of estimating required parameters from traffic detector data. The basic model was developed by combining ideas from the theory of Markov population processes with a new for the relationship between traffic flow and density, producing a stochastic version of a simple-continuum model. Kalman filtering was then applied to the basic model to develop algorithms for (1) estimating from loop detector counts the traffic density in freeway sections broken down by destination off-ramp, (2) predicting main-line and off-ramp traffic volumes from given on-ramp volumes and, (3) computing adaptive estimates of the freeway's origin destination matrix from loop detector counts. Monte Carlo simulation tests were used to evaluate three different methods for off-line estimation of model parameters, as well as to assess the accuracy of the density estimates and volume predictions. The results indicated that the estimation and prediction model tends to be robust with respect to the parameter estimation scheme, and that the model generates a reasonable characterization of estimation and prediction uncertainty. Limited tests with field data tended to confirm the simulation results, and to emphasize the importance of real-time estimation of freeway origin-destination matrices in generating accurate predictions.