Showing papers in "Transportation Research Part C-emerging Technologies in 2006"
TL;DR: It is shown that a near-global solution to the resulting nonlinear optimization problem can be found by solving a single linear program, whenever certain conditions are met.
Abstract: The onramp metering control problem is posed using a cell transmission-like model called the asymmetric cell transmission model (ACTM). The problem formulation captures both freeflow and congested conditions, and includes upper bounds on the metering rates and on the onramp queue lengths. It is shown that a near-global solution to the resulting nonlinear optimization problem can be found by solving a single linear program, whenever certain conditions are met. The most restrictive of these conditions requires the congestion on the mainline not to back up onto the onramps whenever optimal metering is used. The technique is tested numerically using data from a severely congested stretch of freeway in southern California. Simulation results predict a 17.3% reduction in delay when queue constraints are enforced.
371 citations
TL;DR: In this article, the authors presented an analysis of real traffic driving patterns connected to the street network in the city of Lund, Sweden and found that the drivers spontaneous choice of route was not the most fuel-efficient.
Abstract: Today, driver support tools intended to increase traffic safety, provide the driver with convenient information and guidance, or save time are becoming more common. However, few systems have the primary aim of reducing the environmental effects of driving. The aim of this project was to estimate the potential for reducing fuel consumption and thus the emission of CO, through a navigation system where optimization of route choice is based on the lowest total fuel consumption (instead of the traditional shortest time or distance), further the supplementary effect if such navigation support could take into account real-time information about traffic disturbance events from probe vehicles running in the street network. The analysis was based on a large database of real traffic driving patterns connected to the street network in the city of Lund, Sweden. Based on 15437 cases, the fuel consumption factor for 22 street classes, at peak and off-peak hours, was estimated for three types of cars using two mechanistic emission models. Each segment in the street network was, on a digitized map, attributed an average fuel consumption for peak and off-peak hours based on its street class and traffic flow conditions. To evaluate the potential of a fuel-saving navigation system the routes of 109 real journeys longer than 5 min were extracted from the database. Using Esri's external program ArcGIS, Arcview and the external module Network Analysis, the most fuel-economic route was extracted and compared with the original route, as well as routes extracted from criterions concerning shortest time and shortest distance. The potential for further benefit when the system employed real-time data concerning the traffic situation through 120 virtual probe vehicles running in the street network was also examined. It was found that for 46% of trips in Lund the drivers spontaneous choice of route was not the most fuel-efficient. These trips could save, on average, 8.2% fuel by using a fuel-optimized navigation system. This corresponds to a 4% fuel reduction for all journeys in Lund. Concerning the potential for real-time information from probe vehicles, it was found that the frequency of disturbed segments in Lund was very low, and thus so was the potential fuel-saving. However, a methodology is presented that structures the steps required in analyzing such a system. It is concluded that real-time traffic information has the potential for fuel-saving in more congested areas if a sufficiently large proportion of the disturbance events can be identified and reported in real-time. (Less)
314 citations
TL;DR: In this article, neighborhood search heuristics are proposed to optimize the planned routes of vehicles in a context where new requests, with a pick-up and a delivery location, occur in real-time.
Abstract: This paper proposes neighborhood search heuristics to optimize the planned routes of vehicles in a context where new requests, with a pick-up and a delivery location, occur in real-time. Within this framework, new solutions are explored through a neighborhood structure based on ejection chains. Numerical results show the benefits of these procedures in a real-time context. The impact of a master–slave parallelization scheme, using an increasing number of processors, is also investigated.
274 citations
TL;DR: In this article, a real-time crash prediction model was developed to estimate crash potential based on short-term variation of traffic flow characteristics and a microscopic traffic simulation model was used to realistically simulate changes in traffic conditions as an effect of variable speed limits and combined with the crash prediction models for the evaluation of control logics.
Abstract: This paper examines automated control strategies of variable speed limits that aim at reducing crash potential on instrumented freeways. A real-time crash prediction model was developed to estimate crash potential based on short-term variation of traffic flow characteristics. A microscopic traffic simulation model was used to realistically simulate changes in traffic conditions as an effect of variable speed limits and combined with the crash prediction model for the evaluation of control logics. Within this integrated evaluation framework, the study investigated the effect of strategy control factors on the crash potential reduction and total travel time. The study results indicated that variable speed limits could reduce crash potential by 5–17%, by temporarily reducing speed limits during risky traffic conditions when crash potential exceeded the pre-specified threshold.
257 citations
TL;DR: The paper first introduces the utilized stochastic macroscopic freeway network traffic flow model and a real-time traffic measurement model, upon which the complete dynamic system model of RENAISSANCE is established with special attention to the handling of some important model parameters.
Abstract: The paper presents a unified macroscopic model-based approach to real-time freeway network traffic surveillance as well as a software tool RENAISSANCE that has been recently developed to implement this approach for field applications. RENAISSANCE is designed on the basis of stochastic macroscopic freeway network traffic flow modeling, extended Kalman filtering, and a number of traffic surveillance algorithms. Fed with a limited amount of real-time traffic measurements, RENAISSANCE enables a number of freeway network traffic surveillance tasks, including traffic state estimation and short-term traffic state prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction, and incident alarm. The traffic state estimation and prediction lay the operating foundation of RENAISSANCE since RENAISSANCE bases the other traffic surveillance tasks on its traffic state estimation or prediction results. The paper first introduces the utilized stochastic macroscopic freeway network traffic flow model and a real-time traffic measurement model, upon which the complete dynamic system model of RENAISSANCE is established with special attention to the handling of some important model parameters. The algorithms for the various traffic surveillance tasks addressed are described along with the functional architecture of the tool. A simulation test was conducted via application of RENAISSANCE to a hypothetical freeway network example with a sparse detector configuration, and the testing results are presented in some detail. Final conclusions and future work are outlined.
136 citations
TL;DR: A conceptual model of departure time choice under travel time uncertainty and information is proposed, based on expected utility theory, and includes the variation in travel time, the quality of travel time information and travellers’ perception of the travel time.
Abstract: A negative effect of congestion that tends to be overlooked is travel time uncertainty. Travel time uncertainty causes scheduling costs due to early or late arrival. The negative effects of travel time uncertainty can be reduced by providing travellers with travel time information, which improves their estimate of the expected travel time, thereby reducing scheduling costs. In order to assess the negative effects of uncertainty and the benefits of travel time information, this paper proposes a conceptual model of departure time choice under travel time uncertainty and information. The model is based on expected utility theory, and includes the variation in travel time, the quality of travel time information and travellers’ perception of the travel time. The model is illustrated by an application to the case of the A2 motorway between Beesd and Utrecht in the Netherlands.
118 citations
TL;DR: Results indicate that the statistical characteristics of traffic volume can be identified from prevailing traffic conditions; for example, volume data exhibit frequent shifts from deterministic to stochastic structures as well as transitions between cyclic and strongly nonlinear behaviors.
Abstract: Short-term traffic volume data are characterized by rapid and intense fluctuations with frequent shifts to congestion. Currently, research in short-term traffic forecasting deals with these phenomena either by smoothing them or by accounting for them by nonlinear models. But, these approaches lead to inefficient predictions particularly when the data exhibit intense oscillations or frequent shifts to boundary conditions (congestion). This paper offers a set of tools and methods to assess on underlying statistical properties of short-term traffic volume data, a topic that has largely been overlooked in traffic forecasting literature. Results indicate that the statistical characteristics of traffic volume can be identified from prevailing traffic conditions; for example, volume data exhibit frequent shifts from deterministic to stochastic structures as well as transitions between cyclic and strongly nonlinear behaviors. These findings could be valuable in the implementation of a variable prediction strategy according to the statistical characteristics of the prevailing traffic volume states.
114 citations
TL;DR: An adaptive control model of a network of signalized intersections is proposed based on a discrete-time, stationary, Markov decision process that incorporates probabilistic forecasts of individual vehicle actuations at downstream inductance loop detectors that are derived from a macroscopic link transfer function.
Abstract: An adaptive control model of a network of signalized intersections is proposed based on a discrete-time, stationary, Markov decision process. The model incorporates probabilistic forecasts of individual vehicle actuations at downstream inductance loop detectors that are derived from a macroscopic link transfer function. The model is tested both on a typical isolated traffic intersection and a simple network comprised of five four-legged signalized intersections, and compared to full-actuated control. Analyses of simulation results using this approach show significant improvement over traditional full-actuated control, especially for the case of high volume, but not saturated, traffic demand.
111 citations
TL;DR: The results suggest that the performance of the integrity method depends on the type of map-matching algorithm and the quality of the digital map data.
Abstract: Map-matching algorithms are used to integrate positioning data with digital road network data so that vehicles can be placed on a road map. However, due to error associated with both positioning and map data, there can be a high degree of uncertainty associated with the map-matched locations. A quality indicator representing the level of confidence (integrity) in map-matched locations is essential for some Intelligent Transport System applications and could provide a warning to the user and provide a means of fast recovery from a failure. The objective of this paper is to determine an empirical method to derive the integrity of a map-matched location for three previously developed algorithms. This is achieved by formulating a metric based on various error sources associated with the positioning data and the map data. The metric ranges from 0 to 100 where 0 indicates a very high level of uncertainty in the map-matched location and 100 indicates a very low level of uncertainty. The integrity method is then tested for the three map-matching algorithms in the cases when the positioning data is from either a stand-alone global positioning system (GPS) or GPS integrated with deduced reckoning (DR) and for map data from three different scales (1:1250, 1:2500, and 1:50 000). The results suggest that the performance of the integrity method depends on the type of map-matching algorithm and the quality of the digital map data. A valid integrity warning is achieved 98.2% of the time in the case of the fuzzy logic map-matching algorithm with positioning data come from integrated GPS/DR and a digital map data with a scale of 1:2500.
104 citations
TL;DR: The real-time urban traffic control algorithm CRONOS has been evaluated on an intersection by comparison of two reference control strategies, a local one and a centralized one, and benefits are obtained on the total number of stops and percentage of stops, especially in comparison with the local strategy.
Abstract: The real-time urban traffic control algorithm CRONOS has been evaluated on an intersection by comparison of two reference control strategies, a local one and a centralized one. Recurrent traffic situations, from peak hour traffic to low traffic, have been studied, and the impact on the traffic from a fluidity point of view has been investigated using various criteria. The average behavior of CRONOS has also been analyzed by crossing the traffic signal colors with traffic variables. Several of the criteria are innovative, thanks to the real-time, accurate video-based traffic data collected. The results show high benefits of CRONOS on the total delay compared to the two reference control strategies, and benefits are also obtained on the total number of stops and percentage of stops, especially in comparison with the local strategy. All traffic situations (peak to low traffic) are concerned by these results. The analysis of the average behavior of CRONOS shows a higher average number of cycles per hour, more global green duration per hour at the center of the intersection, to the detriment of the entries. Moreover, CRONOS switches more often from amber to red when no vehicles are present on the link in percentage of cycles or in number of cycles per hour; it switches more often from green to amber when vehicles are present on the link in percentage of cycles per hour.
103 citations
TL;DR: The unique feature of the TSC_ar algorithm is that both the detection rate and false alarm rate are not sensitive to the incident decision thresholds, which suggests that the Bayesian network approach is advanced in enabling effective arterial road incident detection.
Abstract: Timely and accurate incident detection is an essential part of any successful advanced traffic management system. The complex nature of arterial road traffic makes automated incident detection a real challenge. Stable performance and strong transferability remain major issues concerning the existing incident detection algorithms. A new arterial road incident detection algorithm TSC_ar is presented in this paper. In this algorithm, Bayesian networks are used to quantitatively model the causal dependencies between traffic events (e.g. incident) and traffic parameters. Using real time traffic data as evidence, the Bayesian networks update the incident probability at each detection interval through two-way inference. An incident alarm is issued when the estimated incident probability exceeds the predefined decision threshold. The Bayesian networks allow us to subjectively build existing traffic knowledge into their conditional probability tables, which makes the knowledge base for incident detection robust and dynamic. Meanwhile, we incorporate intersection traffic signals into traffic data processing. A total of 40 different types of arterial road incidents are simulated to test the performance of the algorithm. The high detection rate of 88% is obtained while the false alarm rate of the algorithm is maintained as low as 0.62%. Most importantly, it is found that both the detection rate and false alarm rate are not sensitive to the incident decision thresholds. This is the unique feature of the TSC_ar algorithm, which suggests that the Bayesian network approach is advanced in enabling effective arterial road incident detection.
TL;DR: The results of the study indicate that even though public transport travel information is highly price sensitive, travelers are willing to pay for it if the information systems provide additional functionality such as real-time information and, to a lesser extent, additional trip planning options.
Abstract: Web-enabled public transport (PT) information systems that combine information on different PT modes, different PT companies and different geographical regions, can be built to improve the accessibility of public transportation. As the potential list of information aspects that can be included in such systems is long, it is interesting to examine the relative importance of different information aspects. This study reports the relative importance travelers attach to a range of information aspects. In addition, the willingness to pay for this information was examined by conducting a stated choice experiment, in which price was traded off against groupings of information aspects. The results of the study indicate that even though public transport travel information is highly price sensitive, travelers are willing to pay for it if the information systems provide additional functionality such as real-time information and, to a lesser extent, additional trip planning options.
TL;DR: An efficient and reliable procedure has been developed for obtaining this geometric definition of the alignment for two-lane rural highways and its application to the M-607 highway, located in Madrid (Spain), is exposed.
Abstract: Detailed studies of traffic safety, as highway design consistency evaluation, require having the geometric definition of the alignment. An efficient and reliable procedure has been developed for obtaining this geometric definition for two-lane rural highways. The method is based on getting data of the highway by means of a GPS receiver mounted in a car and the subsequent processing of this information. The data taken in the highway are differentially corrected and points in the roadway centerline are estimated by means of a developed calculation algorithm. Finally, the highway alignment is defined by means of a parametric cubic smoothing spline. In this paper, the developed method and its application to the M-607 highway, located in Madrid (Spain), is exposed.
TL;DR: The model provides numerous insights and can be a useful tool in producing robust control and management strategies that account for uncertainty in applications where SO-DTA is relevant (e.g. evacuation modeling, computing alternate routes around freeway incidents and establishing lower bounds on network performance).
Abstract: This paper is concerned with the system optimum-dynamic traffic assignment (SO-DTA) problem when the time-dependent demands are random variables with known probability distributions. The model is a stochastic extension of a deterministic linear programming formulation for SO-DTA introduced by Ziliaskopoulos (Ziliaskopoulos, A.K., 2000. A linear programming model for the single destination system optimum dynamic traffic assignment problem, Transportation Science, 34, 1–12). The proposed formulation is chance-constrained based and we demonstrate that it provides a robust SO solution with a user specified level of reliability. The model provides numerous insights and can be a useful tool in producing robust control and management strategies that account for uncertainty in applications where SO-DTA is relevant (e.g. evacuation modeling, computing alternate routes around freeway incidents and establishing lower bounds on network performance).
TL;DR: The design phase of an infrastructure-based intersection decision support (IDS) system to help drivers make safer gap acceptance decisions at rural intersections is outlined and the appropriate design specifications for initial testing of the IDS system interface are discussed.
Abstract: Rural, stop-controlled intersections pose a crash risk to drivers, particularly elderly drivers. This paper outlines the design phase of an infrastructure-based intersection decision support (IDS) system to help drivers make safer gap acceptance decisions at rural intersections. A human factors-based design process was conducted to determine the type of information that should be presented to drivers. Information considered important for presentation to the driver included showing the presence of gaps, indicating the size of available gaps, and/or judging the safety of available gaps. This paper discusses the process used to determine the appropriate design specifications for initial testing of the IDS system interface.
TL;DR: A macroscopic model with velocity saturation for traffic flow in which each individual vehicle is controlled by an adaptive cruise control spacing policy is presented and Quantitative relationships between traffic flow stability and model parameters (such as traffic flow and speed, etc.) are derived.
Abstract: Traffic flow propagation stability is concerned about whether a traffic flow perturbation will propagate and form a traffic shockwave. In this paper, we discuss a general approach to the macroscopic traffic flow propagation stability for adaptive cruise controlled (ACC) vehicles. We present a macroscopic model with velocity saturation for traffic flow in which each individual vehicle is controlled by an adaptive cruise control spacing policy. A nonlinear traffic flow stability criterion is investigated using a wavefront expansion technique. Quantitative relationships between traffic flow stability and model parameters (such as traffic flow and speed, etc.) are derived for a generalized ACC traffic flow model. The newly derived stability results are in agreement with previously derived results that were obtained using both microscopic and macroscopic models with a constant time headway (CTH) policy. Moreover, the stability results derived in this paper provide sufficient and necessary conditions for ACC traffic flow stability and can be used to design other ACC spacing policies.
TL;DR: The results showed that delay estimations by the ANNDE and NFDE model are promising, and it is inferred that theNFDE model results are the best fitted.
Abstract: Modeling vehicle delay has been an interesting subject for traffic engineers and urban planners. Determination of vehicle delay is a complex task and the delay is influenced by many variables that have uncertainties and vagueness, especially for non-uniform or over-saturated conditions. In this study, vehicle delay is modeled using new approaches such as Fuzzy Logic (FL) and Artificial Neural Networks (ANN) to deal with all conditions. The Neuro Fuzzy Delay Estimation (NFDE) model and Artificial Neural Networks Delay Estimation (ANNDE) model are developed. The overall delay data required for the model were collected from ten signalized intersections in Turkey. The results of the developed models are compared with the Highway Capacity Manual (HCM), Akcelik’s methods and the delay data collected from intersections. The results showed that delay estimations by the ANNDE and NFDE model are promising. It is also inferred that the NFDE model results are the best fitted. The Average Relative Error (ARE) rates of NFDE model are determined as 7% for under-saturated and 5% for over-saturated conditions. The results reflect the fact that the neuro-fuzzy approach may be used as a promising method in vehicle delay estimation.
TL;DR: An overview of motion-based methods used in a system developed as part of a EU-funded research project, to detect three important situations of interest to public transport operators, as assessed with a large set of video recordings supplied by metropolitan railway networks in London, Paris and Milan.
Abstract: The timely detection of potentially dangerous situations involving passengers in public transport sites is vital to improve the safety and confidence of the travelling public. Conventional CCTV systems are monitored manually so that a single observer is typically responsible for dealing with tens or hundreds of cameras at a time. Thus, important events might be missed or detected too late for effective action. This paper gives an overview of motion-based methods used in a system developed as part of a EU-funded research project, to detect three important situations of interest to public transport operators. The style has been kept intentionally general so as to provide a good broad understanding of the transport needs being addressed. Emphasis is given to the performance of these methods as assessed with a large set of video recordings supplied by metropolitan railway networks in London, Paris and Milan.
TL;DR: In this paper, the authors introduce the concept of total airport performance analysis and describe the development of a Decision Support System capable of performing integrated airport analysis, and demonstrate the capabilities of this decision support system by analyzing a real-world airport planning case of the Athens International Airport.
Abstract: Airport decision makers are frequently facing complex decision-making problems related to airport planning, design, and operations. The airport decision-making process is further perplexed by the large number of stakeholders having different, and sometimes conflicting, objectives regarding the assessment of the airport performance. Despite the rich experience in both models and tools for airport performance analysis, existing models and tools address only fragmented parts of the airport decision-making process. At present, airport stakeholders lack models and tools able to provide an integrated view of the total airport processes and analyze the tradeoffs between the various measures of airport effectiveness. The objective of this paper is threefold: (i) to introduce the concept of total airport performance analysis, (ii) to describe the development of a Decision Support System capable of performing integrated airport analysis, and (iii) to demonstrate the capabilities of this Decision Support System by analyzing a real-world airport planning case of the Athens International Airport.
TL;DR: Results suggest that the behavior-based consistency-seeking BBCS model can reasonably capture the within-day variations in driver behavior model parameters and class fractions in the traffic stream and indicate that deployment-capable information strategies can be used to influence system performance.
Abstract: This paper proposes a behavior-based consistency-seeking (BBCS) model as an alternative to the dynamic traffic assignment paradigm for the real-time control of traffic systems under information provision. The BBCS framework uses a hybrid probabilistic–possibilistic model to capture the day-to-day evolution and the within-day dynamics of individual driver behavior. It considers heterogeneous driver classes based on the broad behavioral characteristics of drivers elicited from surveys and past studies on driver behavior. Fuzzy logic and if–then rules are used to model the various driver behavior classes. The approach enables the modeling of information characteristics and driver response to be more consistent with the real-world. The day-to-day evolution of driver behavior characteristics is reflected by updating the appropriate model parameters based on the current day’s experience. The within-day behavioral dynamics are reactive and capture drivers’ actions vis-a-vis the ambient driving conditions by updating the weights associated with the relevant if–then rules. The BBCS model is deployed by updating the ambient driver behavior class fractions so as to ensure consistency with the real-time traffic sensor measurements. Simulation experiments are conducted to investigate the real-time applicability of the proposed framework to a real-world network. The results suggest that the approach can reasonably capture the within-day variations in driver behavior model parameters and class fractions in the traffic stream. Also, they indicate that deployment-capable information strategies can be used to influence system performance. From a computational standpoint, the approach is real-time deployable.
TL;DR: Both the detection rate and false alarm rate of the TSC algorithm are not sensitive to the incident decision threshold, which greatly improves the stability of incident detection.
Abstract: This paper reports the intensive test of the new transport systems centre (TSC) algorithm applied to incident detection on freeways. The TSC algorithm is designed to fulfil the universality expectations of automated incident detection. The algorithm consists of two modules: data processing module and incident detection module. The data processing module is designed to handle specific features of different sites. The Bayesian network based incident detection module is used to store and manage general expert traffic knowledge, and to perform coherent reasoning to detect incidents. The TSC algorithm is tested using 100 field incident data sets obtained from Tullamarine Freeway and South Eastern Freeway in Melbourne, Australia. The performance of the algorithm demonstrates its competitiveness with the best performing neural network algorithm which was developed and tested using the same incident data sets in an early research. Most importantly, both the detection rate and false alarm rate of the TSC algorithm are not sensitive to the incident decision threshold, which greatly improves the stability of incident detection. In addition, a very consistent algorithm performance is achieved when the TSC algorithm is transferred from Southern Expressway of Adelaide to both Tullamarine Freeway and South Eastern Freeway of Melbourne. No substantial algorithm retraining is required. A significant step towards algorithm universality is possible from this research.
TL;DR: In this article, a Bayesian statistical inference algorithm is used to determine the distributions of multiple parameters of a fleet of quarter-car heavy vehicle ride models, based on prior assumed distributions and the set of observed dynamic tyre force from a ‘true’ fleet of one hundred simulated models.
Abstract: Statistical spatial repeatability (SSR) is an extension to the well known concept of spatial repeatability. SSR states that the mean of many patterns of dynamic tyre force applied to a pavement surface is similar for a fleet of trucks of a given type. A model which can accurately predict patterns of SSR could subsequently be used in whole-life pavement deterioration models as a means of describing pavement loading due to a fleet of vehicles. This paper presents a method for predicting patterns of SSR, through the use of a truck fleet model inferred from measurements of dynamic tyre forces. A Bayesian statistical inference algorithm is used to determine the distributions of multiple parameters of a fleet of quarter-car heavy vehicle ride models, based on prior assumed distributions and the set of observed dynamic tyre force from a ‘true’ fleet of one hundred simulated models. Simulated forces are noted at 16 equidistant pavement locations, similar to data from a multiple sensor weigh-in-motion site. It is shown that the fitted model provides excellent agreement in the mean pattern of dynamic force with the originally generated truck fleet. It is shown that good predictions are possible for patterns of SSR on a given section of road for a fleet of similar vehicles. The sensitivity of the model to errors in parameter estimation is discussed, as is the potential for implementation of the method.
TL;DR: An effective approach to design, implement, and operate a traffic control system based on logic programming, using the Leibniz System, which is capable of generating fast solution algorithms for the decision problems associated with traffic signal setting.
Abstract: In this paper we describe an effective approach to design, implement, and operate a traffic control system based on logic programming. With this approach it is possible to implement very flexible control strategies that can be easily developed by traffic engineers using a simple description language. An important feature of the system is the use of a very efficient logic programming solver, the Leibniz System, which is capable of generating fast solution algorithms for the decision problems associated with traffic signal setting. A micro-simulator has been developed to verify the effectiveness of the method. It is a crucial tool of an integrated development system, that allows one to develop control strategies and to test them before their on-field implementation. An application to a real case is described and experimental results are presented.
TL;DR: Several small-scale queuing models are developed to illustrate the importance of incorporating information flow related attributes into the models of transportation systems operations and it is found that ignoring information flows may lead to significant inaccuracies in the estimates of the system performance.
Abstract: Modeling transportation systems operations in large part involves an understanding of how physical entities (i.e., vehicles) move and interact with each other in the system. Transportation systems that are integrated with information technologies involve flow of information besides the flow of physical entities. In some cases, a unified modeling approach that considers both flows is needed to create an accurate model for system operations. This paper highlights the significance of such a modeling approach that involves an explicit representation of information flow attributes (e.g., response time and information delay). Several small-scale queuing models are developed to illustrate the importance of incorporating information flow related attributes into the models of transportation systems operations. In each example system, two scenarios are considered: modeling the given system with or without explicitly representing the information flow. Comparison of performance statistics is made between these two scenarios. It is found that ignoring information flows may lead to significant inaccuracies in the estimates of the system performance.
TL;DR: This study presents two robust algorithms, one for estimation of an initial O–D set and the other for tackling the input measurement errors with an extended estimation algorithm.
Abstract: Most existing dynamic origin–destination (O–D) estimation approaches are grounded on the assumption that a reliable initial O–D set is available and traffic volume data from detectors are accurate. However, in most traffic systems, both types of critical information are either not available or subjected to some level of measurement errors such as traffic counts and speed measurement from sensors. To contend with those critical issues, this study presents two robust algorithms, one for estimation of an initial O–D set and the other for tackling the input measurement errors with an extended estimation algorithm. The core concept of the initial O–D estimation algorithm is to decompose the target network in a number of sub-networks based on proposed rules, and then execute the estimation of the initial O–D set iteratively with the observable information at the first time interval. To contend with the inevitable detector measurement error, this study proposes an interval-based estimation algorithm that converts each model input data as an interval with its boundaries being set based on some prior knowledge. The performance of both proposed algorithms has been tested with a simulated system, the I-95 freeway corridor between I-495 and I-695, and the results are quite promising.
Journal Article•
TL;DR: A general Kalman-filter based model estimation method for car-following dynamics in traffic simulation.
Abstract: A general Kalman-filter based model estimation method for car-following dynamics in traffic simulation