Browsing by Subject "Traffic flow"

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Development of a Road Condition Recovery Time Estimation System for Winter Snow Events
(Minnesota Department of Transportation, 2018-01) Kwon, Eil; Park, Chongmyung
This research develops a Normal Condition Regain Time (NCRT) estimation system, which automatically determines the NCRT at detector stations on the metro-freeway network for given snow events. The NCRT process is based on the findings that the speed level during the recovery process reaches a stable free-flow-speed (FFS), whose value is generally lower than the pre-snow FFS at a same location. Further, the speed-density (U-K) relationship of the traffic flow after snow is cleared exhibits a similar but shifted-down pattern of the normal-day U-K relationship at a given location. In this study, the after-snow traffic condition with a stable but shifted-sown pattern of the normal-day U-K relationship is defined as the ‘wet-normal’ condition, and the NCRT is defined as the time when the U-K data during a snow event starts to follow the wet-normal U-K pattern at a given station. The NCRT estimation system first collects the traffic and weather data for the metro-freeway network and determines the normal-day U-K relationships for the detector stations whose traffic data include both uncongested and congested regions. The normal-day U-K relationships are then applied to calibrate the wet-normal U-K patterns at given locations using the traffic data collected during snow events. Finally, the NCRTs are determined for each station by comparing the U-K data trajectory during a given event with the wet-normal U-K pattern at given locations. The NCRT estimation system has been applied to a set of the sample snow events.
Development of a Travel-Time Reliability Measurement System
(Minnesota Department of Transportation, 2018-09) Kwon, Eil; Park, Chongmyung
This study has developed a computerized Travel-Time Reliability Measurement System (TTRMS), which can automate the time-consuming process of gathering and managing data from multiple sources and calculating various types of reliability measures under user-specified conditions for given corridors. The TTRMS adopts a server and client structure, where the main database and computational engines reside in the server, while the user- clients are designed for entering the data and generating the output files. In particular, most of the external data, such as traffic and weather datasets, can be remotely downloaded following predefined time schedules. Further, the travel-time calculation process developed in this study can explicitly reflect various lane-configurations at work zones for correctly calculating travel times of the routes with work zones. The map-based user interfaces provide users with a flexible environment, where the route selection and specification of operating conditions for reliability estimation can be efficiently performed. The integrated TTRMS was tested in the Twin Cities’ metro freeway network by estimating the reliability measures of selected corridors with real data for a two-year period, 2012-13. The test results indicate that the TTRMS can substantially reduce the time and effort in estimating various types of reliability measures under different operating conditions for predefined corridors.
Development of Advanced Traffic Flow Models and Implementation in Parallel Processing, Phase II (9/15/92-9/15/93)
(Center for Transportation Studies, University of Minnesota, 1994-02) Lyrintzis, Anastasios S.; Michalopoulos, Panos G.; Liu, Guoqing; Rangiah, Raja P.
In this report, five high-order continuum traffic flow models are compared: Payne's model; Papageorgiou's model; the semi-viscous model and the viscous model as well as a proposed high-order model, and the simple continuum model. The stability of the high-order models is analyzed and the shock structure investigated in all models. In addition, the importance of the proper choice of finite-difference method is addressed. For this reason, three explicit finite-difference methods for numerical implementation, namely, the Lax method, the explicit Euler method and the upwind scheme with flux vector splitting, are discussed. The test with hypothetical data and the comparison of numerical results with field data suggest that high-order models implemented through the upwind method are better than the simple continuum model. The proposed high-order model appears to be more accurate than the other high-order models.
Development of On-Line Control Strategies in Freeway Networks, Phase 2: Final Report
(Minnesota Department of Transportation, 1998-05) Stephanedes, Yorgos J.; Liu, Xiao; Liu, Lu; Michel, Bernard R.
Most traffic-responsive freeway ramp metering systems select metering rates from predetermined rate libraries. The efficiency of such systems is impaired by the lack of an efficient analysis tool that can evaluate and update the thresholds and rate libraries used by the meter controllers. In this project, a control-emulation method is developed to evaluate various automatic rateselection strategies; the new modeling features of this system are described in detail. Various rate selection strategies (based on neural network processing, exit ramp volume, and real time bottleneck/dynamic zone determination) are described and evaluated in comparison with the current Minneapolis-St. Paul strategy. An online traffic volume predictor based on Kalman filtering is developed, and integrated into the control-emulation module. A simulated annealing optimization algorithm, previously implemented on a supercomputer, is re-implemented on a personal computer and integrated into the simulation module.
Development of the Next Generation Stratified Ramp Metering Algorithm Based on Freeway Density
(Center for Transportation Studies, 2011-03) Geroliminis, Nikolas; Srivastava, Anupam; Michalopoulos, Panos
A 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.
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.
Estimation of Winter Snow Operation Performance Measures with Traffic Data
(Minnesota Department of Transportation, 2012-12) Kwon, Eil; Hong, Seongah; Kim, Soobok; Jeon, Soobin
This research produced an automatic process to identify the road condition recovered times during snow events from the traffic-flow data. For this study, the traffic data from the past snow events were analyzed and the speed variation patterns indicating the road condition recovery states during the recovery periods were identified. The prototype process developed in this study finds the speed change point indicating the recovery of the road condition by analyzing the speed variations for a given location. The process was then applied to a set of the past snow events and the estimated recovered times were compared with the reported lane-regain time data.
Improving intersection safety through variable speed limits for connected vehicles
(Center for Transportation Studies, University of Minnesota, 2019-05) Levin, Michael; Chen, Rongsheng; Liao, Chen-Fu; Zhang, Tab
Autonomous vehicles create new opportunities for innovative intelligent traffic systems. Variable speed limits, which is a speed management systems that can adjust the speed limit according to traffic condition or predefined speed control algorithm on different road segments, can be better implemented with the cooperation of autonomous vehicles. These compliant vehicles can automatically follow speed limits. However, non-compliant vehicles will attempt to pass the moving bottleneck created by the compliant vehicle. This project builds a multi-class cell transmission model to represent the relation between traffic flow parameters. This model can calculate flows of both compliant and non-compliant vehicles. An algorithm is proposed to calculate variable speed limits for each cell of the cell transmission model. This control algorithm is designed to reduce the stop-and-go behavior of vehicles at traffic signals. Simulation is used to test the effects of VSLs on an example network. The result shows that VSL is effective at reducing the energy consumption of the whole system and reduce the likelihood of crash occurrence.
Network Econometrics and Traffic Flow Analysis
(2016-10) Ermagun, Alireza
This dissertation introduces concepts, theories, and methods dealing with network econometrics to gain a deeper understanding of how the components interact in a complex network. More precisely, it introduces distinctive network weight matrices to extract the existing spatial dependency between traffic links. The network weight matrices stem from the concepts of betweenness centrality and vulnerability in network science. Their elements are a function not simply of proximity, but of network topology, network structure, and demand configuration. The network weight matrices are tested in congested and uncongested traffic conditions in both simulation-based and real-world environments. The results of the analysis lead to a conclusion that traditional spatial weight matrices are unable to capture the realistic spatial dependency between traffic links in a network. Not only do they overlook the competitive nature of traffic links, but they also ignore the role of network topology and demand configuration in measuring the spatial dependence between traffic links. However, the proposed network weight matrices substitute for traditional spatial weight matrices and exhibit the capability to overcome these deficiencies. The network weight matrices are theoretically defensible in account of acknowledging traffic theory. They capture the competitive and complementary nature of links and embed additional network dynamics such as cost of links and demand configuration. Building on real-world data analysis, the results contribute to the conclusion that in a network comprising links in parallel and series, both negative and positive correlations show up between links. The strength of the correlation varies by time-of-day and day-of-week. Strong negative correlations are observed in rush hours, when congestion affects travel behavior. This correlation occurs mostly in parallel links, and in far upstream links where travelers receive information about congestion (for instance from media, variable message signs, or personal observations of propagating shockwaves) and are able to switch to substitute paths. Irrespective of time-of-day and day-of-week, a strong positive correlation is observed between upstream and downstream sections. This correlation is stronger in uncongested regimes, as traffic flow passes through the consecutive links in a shorter time and there is no congestion effect to shift or stall traffic.
Non-linear spacing policy and network analysis for shared-road platooning
(Center for Transportation Studies, University of Minnesota, 2019-08) Levin, Michael; Rajamani, Rajesh; Jeon, Woongsun; Chen, Rongsheng; Kang, Di
Connected vehicle technology creates new opportunities for obtaining knowledge about the surrounding traffic and using that knowledge to optimize individual vehicle behaviors. This project creates an interdisciplinary group to study vehicle connectivity, and this report discusses three activities of this group. First, we study the problem of traffic state (flows and densities) using position reports from connected vehicles. Even if the market penetration of connected vehicles is limited, speed information can be inverted through the flow-density relationship to estimate space-and time-specific flows and densities. Propagation, according to the kinematic wave theory, is combined with measurements through Kalman filtering. Second, the team studies the problem of cyber-attack communications. Malicious actors could hack the communications to incorrectly report position, speed, or accelerations to induce a collision. By comparing the communications with radar data, the project team develops an analytical method for vehicles using cooperative adaptive cruise control to detect erroneous or malicious data and respond accordingly (by not relying on connectivity for safe following distances). Third, the team considers new spacing policies for cooperative adaptive cruise control and how they would affect city traffic. Due to the computational complexity of microsimulation, the team elects to convert the new spacing policy into a flow-density relationship. A link transmission model is constructed by creating a piecewise linear approximation. Results from dynamic traffic assignment on a city network shows that improvements in capacity reduces delays on freeways, but surprisingly route choice increased congestion for the overall city.
Operational Evidence of Changing Travel Patterns
(Institute of Transportation Engineers, 1994) Levinson, David M; Kumar, Ajay
This paper utilizes a traffic counts database covering a ten year period (1976-1985) to identify travel trends for Montgomery County, a suburb of Washington D.C. Generally, travel behavior is analyzed using person based travel survey data. The use of traffic counts to understand travel behavior is a relatively new approach. Unlike household surveys, which are typically characterized by respondent and sample bias, and require special effort for their collection, traffic counts are routinely collected by Departments of Transportation and provide the best available measure of observed traffic volumes. The study provides fresh evidence to support some of the earlier findings: an increase in lateral commuting as a share of travel, changes in work and non-work trip proportions, and increase in peak spreading. An interesting result in this paper relates to a more pronounced directionality in radial as compared with lateral trips. The relative symmetry of traffic flows along lateral routes compared with radial routes results in better utilization of the suburban road network. Non-work trips emerge as the more elastic trips, shifting to off-peak hours with an increase in congestion.
Planning, Operation, and Management of Automated Transportation Systems: A Control-Theoretic Approach
(2022-12) Wang, Shian
With the advent of emerging technologies like 5G network and wireless communication, automated vehicles (AVs) are expected to become increasingly available to travelers, offering a vast amount of benefits, such as enhanced traffic stability, reduced energy consumption, and optimized parking space allocation, among many others. It is highly anticipated that there will be a transitional period of the the auto market as human-driven vehicles (HVs) are gradually replaced by AVs. Many opportunities and challenges are expected to emerge during this transitioning process. To better prepare a nation for the arrival of AVs, in this dissertation we aim to address interesting yet pressing problems arising from vehicle automation in the context of planning, operation, and management of future transportation systems from a control-theoretic perspective. In view of the inevitable coexistence of HVs and AVs during the transitioning period, we develop a continuous-time dynamical model to capture the interactive temporal evolution of the market share of these two types of vehicles. A discrete choice model is constructed and incorporated into the dynamical model for describing the likelihood of customers choosing HVs or AVs. To achieve a desired temporal integration of AVs into the auto market, monetary subsidies and investment in AV-specific infrastructure are considered as decision variables to promote the adoption of AVs. Further, an optimal control problem is formulated with the objective of achieving a desired market penetration rate (MPR) at the end of any given finite planning horizon, while minimizing the cost of AV subsidies and infrastructure investment. The time-dependent optimal AV integration policy is determined by solving the formulated optimization problem, allowing a government agency to subsidize AV purchases and invest in future transportation infrastructure in an adaptive manner. The proposed approach is observed to be effective and robust under various demand patterns, such as increasing, decreasing, and stochastic demands. A systematic cost-benefit analysis with sensitivity analysis is conducted to evaluate the desirability of AV integration. The promising results provide significant managerial insights for government agencies into developing long-term strategic planning policies for the integration of AVs. Although appropriate incentive policies could accelerate the adoption of AVs, the MPR is expected to remain relatively low in the next thirty years or so, resulting in a predominantly human-driven mixed traffic flow consisting of HVs and AVs. Uniform traffic flow has been shown to be unstable in certain flow regimes due to collective behavior of human drivers, causing the well-observed stop-and-go waves. These traffic waves can arise even in the absence of merges, bottlenecks, or lane changing, and likely result in more energy consumption and emissions. Taking advantage of vehicle automation, we develop an approach to smoothing unstable traffic flow via optimal control of a small proportion of AVs in a predominantly human-driven traffic flow. These controlled AVs act as mobile actuators in mixed-autonomy traffic without changing the way HVs normally operate. We develop a general framework to describe mixed traffic flow with its dynamics abiding by car-following principles. Based on this framework, we synthesize optimal feedback controllers for AVs with the objective of minimizing speed disturbance, thereby resulting in smoother traffic. Following the necessary conditions of optimality prescribed by the Pontryagin's minimum principle, we present a computational algorithm for determining the optimal AV control strategy. The general framework is further illustrated using the intelligent driver model (IDM) and optimal velocity with relative velocity (OVRV) model for HVs and AVs, respectively, to show the effectiveness of the proposed approach on traffic smoothing, as well as the improvement on vehicle fuel economy and emissions. While the optimal AV controller synthesized above is shown to be effective in smoothing unstable mixed traffic, its performance on improving traffic stability is yet to be proven analytically and car-following safety is ensured in a fairly conservative manner. To address these challenging issues, we synthesize appropriate feedback controllers for AVs leveraging nonlinear stability theory. Specifically, we are interested to analytically synthesize appropriate feedback controllers of AVs for smoothing nonlinear mixed traffic in its general functional forms, covering a broad class of deterministic car-following models commonly seen in the literature. Essentially, AVs are controlled to operate in such a way that they closely track a virtual speed profile, i.e., a subtler version of the disturbance resulting from the immediately preceding vehicle. Thus, traffic waves are reduced when propagating backward across controlled AVs. Based on the general functional form of car-following dynamics, we derive a class of effective additive AV controllers that are proven to be able to ensure convergence in speed tracking, leading to smoother traffic. In addition, a set of sufficient conditions is devised for guaranteeing car-following safety. Notably, unlike many existing studies the feedback controllers synthesized require only local traffic information without having to rely on high degrees of vehicle connectivity, and the rate of traffic smoothing is readily tunable, which is useful for practical implementation. The proposed approach is further illustrated with a theoretical IDM and commercially available adaptive cruise control (ACC) vehicles represented by a well-calibrated IDM. In spite of the benefits promised by AVs like enhancing traffic stability shown above, emerging AV technologies open a door for cyberattacks, where a select number of AVs are compromised to drive in an adversarial manner. This could result in a network-wide increase in traffic congestion and vehicle fuel consumption, degrading the performance of transportation systems. Hence, developing effective attack mitigation strategies for AVs is critically important as AVs gradually become a reality. To this end, we derive optimal feedback control law for AVs in the presence of cyberattacks. Notably, attacks are only assumed to have a bounded magnitude (for remaining stealthy) without being subject to any specific probability distribution, which is not only of theoretical interest but also relaxes the assumptions of prior studies. More importantly, to deal with lack of knowledge of malicious attacks, we, for the first time, formulate a min-max control problem to minimize the worst-case potential disturbance to traffic flow. Specifically, under the framework of mixed-autonomy traffic presented before we consider two types of cyberattacks on AVs, namely false data injection attack on sensor measurements and malicious attack on AV control commands. Further, we derive a set of necessary conditions of optimality for the min-max control problem, based on which an iterative computational algorithm is developed for determining the optimal control (driving) strategy of AVs in a decentralized manner. The effectiveness of the proposed approach is demonstrated via numerical simulation considering different levels of attack severity.
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-Gyu
It 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.
A stochastic model of macroscopic traffic flow: theoretical foundations
(2012-08) Jabari, Saif Eddin
In this thesis, a new stochastic extension of Godunov scheme based traffic flow dynamics is developed using a queuing theoretic approach. In contrast to the common approach of adding noise to deterministic models of traffic flow, the present approach considers probabilistic vehicle inter-crossing times (time headways) at various positions along the road as the source of randomness. Subsequently, time headways are used to describe stochastic vehicle counting processes. These counting processes represent the boundary flows in stochastic conservation equations of traffic flow. The advantage of this approach is that (i) non-negativity of time varying traffic variables (namely, traffic densities) is implicitly ensured, and (ii) the mean dynamic of the stochastic model is the Godunov scheme itself. Neither issue has been addressed in previous stochastic modeling approaches which extend the Godunov scheme and its special case, the cell transmission model. A Gaussian approximation of the queueing model is also proposed for purposes of model tractability. The Gaussian approximation is characterized by deterministic mean and covariance dynamics; the mean dynamics are those of the Godunov scheme. By deriving the Gaussian model, as opposed to assuming Gaussian noise arbitrarily, covariance matrices of traffic variables follow from the physics of traffic flow and can be computed using only few parameters, regardless of system size or how finely the system is discretized. Stationary behavior of the covariance function is analyzed and it is shown that the covariance matrices are bounded. Consequently, estimated covariance matrices are also bounded. As a result, Kalman filters that use the proposed model are stochastically observable, which is a critical issue in real time estimation of traffic dynamics. Model validation was carried out in a real-world signalized arterial setting, where cycle-by-cycle maximum queue sizes were estimated using the Gaussian model as a description of state dynamics in a Kalman filter. The estimated queue sizes were compared to observed maximum queue sizes and the results indicate very good agreement between estimated and observed queue sizes.

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