Showing papers in "Transportation Research Part C-emerging Technologies in 2007"

Current map-matching algorithms for transport applications: State-of-the art and future research directions

Abstract: Map-matching algorithms integrate positioning data with spatial road network data (roadway centrelines) to identify the correct link on which a vehicle is travelling and to determine the location of a vehicle on a link. A map-matching algorithm could be used as a key component to improve the performance of systems that support the navigation function of intelligent transport systems (ITS). The required horizontal positioning accuracy of such ITS applications is in the range of 1 m to 40 m (95%) with relatively stringent requirements placed on integrity (quality), continuity and system availability. A number of map-matching algorithms have been developed by researchers around the world using different techniques such as topological analysis of spatial road network data, probabilistic theory, Kalman filter, fuzzy logic, and belief theory. The performances of these algorithms have improved over the years due to the application of advanced techniques in the map matching processes and improvements in the quality of both positioning and spatial road network data. However, these algorithms are not always capable of supporting ITS applications with high required navigation performance, especially in difficult and complex environments such as dense urban areas. This suggests that research should be directed at identifying any constraints and limitations of existing map matching algorithms as a prerequisite for the formulation of algorithm improvements. The objectives of this paper are thus to uncover the constraints and limitations by an in-depth literature review and to recommend ideas to address them. This paper also highlights the potential impacts of the forthcoming European Galileo system and the European Geostationary Overlay Service (EGNOS) on the performance of map matching algorithms. Although not addressed in detail, the paper also presents some ideas for monitoring the integrity of map-matching algorithms. The map-matching algorithms considered in this paper are generic and do not assume knowledge of ‘future’ information (i.e. based on either cost or time). Clearly, such data would result in relatively simple map-matching algorithms.

Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: A case study from Israel

Abstract: The purpose of this paper is to examine the performance of a new operational system for measuring traffic speeds and travel times which is based on information from a cellular phone service provider. Cellular measurements are compared with those obtained by dual magnetic loop detectors. The comparison uses data for a busy 14 km freeway with 10 interchanges, in both directions, during January–March of 2005. The dataset contains 1 284 587 valid loop detector speed measurements and 440 331 valid measurements from the cellular system, each measurement referring to a 5 min interval. During one week in this period, 25 floating car measurements were conducted as additional comparison observations. The analyses include visual, graphical, and statistical techniques; focusing in particular on comparisons of speed patterns in the time–space domain. The main finding is that there is a good match between the two measurement methods, indicating that the cellular phone-based system can be useful for various practical applications such as advanced traveler information systems and evaluating system performance for modeling and planning. 2007 Elsevier Ltd. All rights reserved.

Integrated driving behavior modeling

Abstract: This paper develops, implements and tests a framework for driving behavior modeling that integrates the various decisions, such as acceleration, lane changing and gap acceptance. Furthermore, the proposed framework is based on the concepts of short-term goal and short-term plan. Drivers are assumed to conceive and perform short-term plans in order to accomplish short-term goals. This behavioral framework supports a more realistic representation of the driving task, since it captures drivers' planning capabilities and allows decisions to be based on anticipated future conditions. An integrated driving behavior model, which utilizes these concepts, is developed. The model captures both lane changing and acceleration behaviors. The driver's short-term goal is defined by the target lane. Drivers who wish to change lanes but cannot change lanes immediately, select a short-term plan to perform the desired lane change. Short-term plans are defined by the various gaps in traffic in the target lane. Drivers adapt their acceleration behavior to facilitate the lane change using the target gap. Hence, inter-dependencies between lane changing and acceleration behaviors are captured.

Predicting electronic toll collection service adoption: An integration of the technology acceptance model and the theory of planned behavior

Abstract: In order to reduce the number of vehicles stuck in congestion, especially for stop-and-go traffic at toll plazas, the establishment of electronic toll collection (ETC) systems has been a hot issue and dominant trend in many countries. Taiwan has joined the crowd, adding an ETC system to its toll roads in early 2006. However, despite the potential benefits for motorists, the utilization rate has been lower than expected during the introductory stage. The objective of this study is to advance our understanding on the critical antecedents of motorists’ intention of ETC service adoption by integrating both technology acceptance model (TAM) and theory of planned behavior (TPB) perspectives. Through empirical data collection and analysis from highway motorists who had not installed on-board units (OBU) for ETC service in Taiwan, we found that system attributes, perceived usefulness and perceived ease of use, indeed, positively engender motorists’ attitudes towards ETC service adoption. Moreover, results also reveal that attitude, subjective norm and perceived behavioral control positively influence the intention of ETC system adoption. Implications for practitioners and researchers, and suggestions for future research are also addressed in this study.

Fuel economy improvements for urban driving : Hybrid vs. intelligent vehicles

Abstract: The quest for more fuel-efficient vehicles is being driven by the increasing price of oil. Hybrid electric powertrains have established a presence in the marketplace primarily based on the promise of fuel savings through the use of an electric motor in place of the internal combustion engine during different stages of driving. However, these fuel savings associated with hybrid vehicle operation come at the tradeoff of a significantly increased initial vehicle cost due to the increased complexity of the powertrain. On the other hand, telematics-enabled vehicles may use a relatively cheap sensor network to develop information about the traffic environment in which they are operating, and subsequently adjust their drive cycle to improve fuel economy based on this information – thereby representing ‘intelligent’ use of existing powertrain technology to reduce fuel consumption. In this paper, hybrid and intelligent technologies using different amounts of traffic flow information are compared in terms of fuel economy over common urban drive cycles. In order to develop a fair comparison between the technologies, an optimal (for urban driving) hybrid vehicle that matches the performance characteristics of the baseline intelligent vehicle is used. The fuel economy of the optimal hybrid is found to have an average of 20% improvement relative to the baseline vehicle across three different urban drive cycles. Feedforward information about traffic flow supplied by telematics capability is then used to develop alternative driving cycles firstly under the assumption there are no constraints on the intelligent vehicle’s path, and then taking into account in the presence of ‘un-intelligent’ vehicles on the road. It is observed that with telematic capability, the fuel economy improvements equal that achievable with a hybrid configuration with as little as 7 s traffic look-ahead capability, and can be as great as 33% improvement relative to the un-intelligent baseline drivetrain. As a final investigation, the two technologies are combined and the potential for using feedforward information from a sensor network with a hybrid drivetrain is discussed. � 2006 Elsevier Ltd. All rights reserved.

An approximate dynamic programming approach for the empty container allocation problem

Abstract: The objective of this study is to demonstrate the successful application of an approximate dynamic programming approach in deriving effective operational strategies for the relocation of empty containers in the containerized sea-cargo industry. A dynamic stochastic model for a simple two-ports two-voyages (TPTV) system is proposed first to demonstrate the effectiveness of the approximate optimal solution obtained through a simulation based approach known as the temporal difference (TD) learning for average cost minimization. An exact optimal solution can be obtained for this simple TPTV model. Approximate optimal results from the TPTV model utilizing a linear approximation architecture under the TD framework can then be compared to this exact solution. The results were found comparable and showed promising improvements over an existing commonly used heuristics. The modeling and solution approach can be extended to a realistic multiple-ports multiple-voyages (MPMV) system. Some results for the MPMV case are shown.

Vehicle reidentification and travel time measurement across freeway junctions using the existing detector infrastructure

Abstract: Conventional vehicle detectors are capable of monitoring discrete points along the freeway but do not provide information about conditions on the link between detectors. Knowledge of conditions on the link is useful to operating agencies for enabling timely decisions in response to various delay causing events and hence to reduce the resulting congestion of the freeway system. This paper presents an approach that matches vehicle measurements between detector stations to provide information on the conditions over the link between the detectors rather than relying strictly on the aggregate point measurements from the detectors. In particular this work reidentifies measurements from distinct vehicles using the existing loop detector infrastructure. Here the distinct vehicles are the long vehicles, but depending on the vehicle population or type of detector used, one might chose to use some other reproducible feature. This new methodology represents an important advancement over preceding loop based vehicle reidentification, as illustrated herein, it enables vehicle reidentification across a major diverge and a major merge. The examples include a case where the reidentification algorithm responded to delay between two detector stations an hour before the delay was locally observable at either of the stations used for reidentification. While previous loop based reidentification work was limited to dual loop detectors, the present effort also extends the methodology to single loop detectors; thereby making it more widely applicable. Although the research uses loop detector data, the algorithm would be equally applicable to data obtained from many other traffic detectors that provide reproducible vehicle features.

Modelling a signal controlled traffic stream using cellular automata

Abstract: Cellular automata models have formed the theory for the development of several transportation models to simulate various types of elements such as vehicles, pedestrians or even railway traffic. Furthermore, they have been applied to simulate several scenarios from very simple (freeway traffic) to rather complicated ones (lane reduction and signal optimisation). However, the properties of the model when used to simulate a signal controlled traffic stream have not been dealt with in great detail. This paper discusses several issues that arise while using the model for the simulation of traffic at signalised intersections. It also investigates the relationships between the randomisation parameter of the model, the model dynamics and the estimated saturation flow. For the deterministic version of the model, the formulas describing traffic quantities at the intersection are derived and are dependent on the desired speed – a parameter of the model. For the stochastic version, one can adopt several different approaches for the application of the randomisation rule, depending on the simulation needs.

A fuzzy-based system for incident detection in urban street networks

Abstract: Detecting incidents on urban streets or arterials using loop detector data is quite challenging. The pattern of the incident could be quite similar to non-incident cases as intersections get congested. This paper describes the development of a fuzzy logic for incident detection. An I ntegrated S ystem for I ncident M anagement ( - sim ) was developed. An integral component of such system is a microscopic simulator, - sim - s , an object-oriented model that allows for virtual detector installations at different locations, modeling different intersection layouts, traffic control types and timing, and link characteristics. - sim - s was utilized to generate various incident scenarios and extracting associated detectors’ accumulative counts. A data clustering technique was utilized to consolidate the various incident scenarios into a single data set for the development of the F uzzy L ogic for incident detection at intersections ( - sim - fl ). The - sim - fl uses the detector data as well as other link properties in flagging detecting incidents. The - sim - fl can be used to indicate the possibility of an incident, a stalled vehicle, or a sort of traffic disturbance. The devised logic was validated using separate simulation-based incident scenarios.

Location configuration design for Dynamic Message Signs under stochastic incident and ATIS scenarios

Abstract: This paper proposes a methodology for deploying permanent Dynamic Message Signs (DMS) in a vehicular traffic network. Of particular interest is the planning problem to optimize the number of DMS to deploy in conjunction with Advanced Traveler Information Systems (ATIS), operating and maintenance cost of DMS, and incident-related user cost under random traffic incident situations. The optimal DMS location design problem discussed herein is formulated as a two-stage stochastic program with recourse (SPR). A Tabu search algorithm combined with dynamic traffic simulation and assignment approaches are employed to solve this problem. A case study performed on the Fort-Worth, Texas network highlights the effectiveness of the proposed framework and illustrates the affect factors such as demand, network structure, DMS response rate, and incident characteristics have on the solution. The numerical results suggest that designing and deploying DMS and ATIS jointly is more cost-effective and efficient than the sequential build-out of the two from the system management perspective.

Estimation of infrastructure performance models using state-space specifications of time series models

Abstract: We consider state-space specifications of autoregressive moving average models (ARMA) and structural time series models as a framework to formulate and estimate inspection and deterioration models for transportation infrastructure facilities. The framework provides a rigorous approach to exploit the abundance and breadth of condition data generated by advanced inspection technologies. From a managerial perspective, the framework is attractive because the ensuing models can be used to forecast infrastructure condition in a manner that is useful to support maintenance and repair optimization, and thus they constitute an alternative to Markovian transition probabilities. To illustrate the methodology, we develop performance models for asphalt pavements. Pressure and deflection measurements generated by pressure sensors and a falling weight deflectometer, respectively, are represented as manifestations of the pavement’s elasticity/load-bearing capacity. The numerical results highlight the advantages of the two classes of models; that is, ARMA models have superior data-fitting capabilities, while structural time series models are parsimonious and provide a framework to identify components, such as trend, seasonality and random errors. We use the numerical examples to show how the framework can accommodate missing values, and also to discuss how the results can be used to evaluate and select between inspection technologies.

Mesoscopic simulation of a dynamic link loading process

Abstract: Passing from path flows to link flows requires non-linear and complex flow propagation models known as network loading models. In specific technical literature, different approaches have been used to study Dynamic Network Loading models, depending on whether the link performances are expressed in an aggregate or disaggregate way, and how vehicles are traced. When vehicle movements are traced implicitly and link performances are expressed in an aggregate way, the approach is macroscopic. When vehicle movements are traced explicitly, two cases are possible, depending on whether link performances are expressed in a disaggregate or aggregate way. In the first case, the approach is microscopic, otherwise it is mesoscopic. In this paper, a mesoscopic Dynamic Network Loading model is considered, based on discrete packets and taking into account the vehicle acceleration and deceleration. A simulation was carried out, first using theoretical input data to simulate over-saturation condition, and then real data to validate the model. The results show that the model appears realistic in the representation of outflow dynamics and is quite easy to calculate. It is worth noting that network loading models are usually used downstream of the assignment models from which they take path flows to calculate link flows. In the above mentioned simulation, we assumed that a generic assignment model provides sinusoidal path flow.

Considering various ALINEA ramp metering strategies for crash risk mitigation on freeways under congested regime

Abstract: This study evaluates the expected benefits of using the ALINEA ramp metering algorithm as a method for real-time safety improvement on an urban freeway. The objective of this research is to use ramp metering to produce a significant decrease in the risk of crashes on the freeway while avoiding any significant adverse effects on operation. This is achieved by simulating the freeway during the congested period in micro-simulation and testing various ramp metering configurations to determine which provides the best results. Statistical measures developed for the same stretch of freeway using loop detector data are used to quantify the risk of crashes as well as the benefits in each of the alternative strategies. The study concludes that there are significant benefits in metering multiple ramps when the feedback ramp metering algorithm is implemented at multiple locations. It was found that increasing the number of metered on-ramps produces increasing safety benefits. Also, a shorter cycle length for each of the meters and a higher critical occupancy value leads to better results.

Validation of a multimodal travel simulator with travel information provision

Abstract: This paper presents a computer based travel simulator for collecting data concerning the use of next-generation ATIS and their effects on traveler decision making in a multimodal travel environment. The tool distinguishes itself by presenting a completely abstract multimodal transport network, where knowledge levels are fully controlled for in terms of awareness of mode-route combinations as well as in terms of variability of characteristics of known alternatives. Furthermore, it contains an information service-module that provides a variety of advanced types of travel information, with controlled for levels of reliability. These novel features warrant an extensive validation of the travel simulator. Such a validation is performed, using two datasets (one with revealed data, one with data from an experiment held in the simulator) obtained among 264 participants. Results suggest that the simulator succeeds in collecting useful data on multimodal travel choice making under provision of advanced types of travel information.

Dynamic Stackelberg equilibrium congestion pricing

Abstract: This paper considers the problem of dynamic congestion pricing that determines optimal time-varying tolls for a pre-specified subset of arcs with bottleneck on a congested general traffic network. A two-person nonzero-sum dynamic Stackelberg game model is formulated with the assumption that the underlying information structure is open loop. Characteristics of the Stackelberg equilibrium solution are analyzed. The Hooke–Jeeves algorithm that obviates an evaluation of the gradient vector of the objective function is presented with a numerical example. The paper concludes with its future extensions.

Classifiers for driver activity monitoring

Abstract: The goal of this work is the detection and classification of driver activities in an automobile using computer vision. To this end, this paper presents a novel two-step classification algorithm, namely, an unsupervised clustering algorithm for grouping the actions of a driver during a certain period of time, followed by a supervised activity classification algorithm. The main contribution of this work is the combination of the two methods to provide a computationally fast solution for deployment in real-world scenarios that is robust to illumination and segmentation issues under most conditions experienced in the automobile environment. The unsupervised clustering groups the actions of the driver based on the relative motion detected using a skin-color segmentation algorithm, while the activity classifier is a binary Bayesian eigenimage classifier. Activities are grouped as safe or unsafe and the results of the classification are shown on several subjects obtained from two distinct driving video sequences.

A Lagrangian relaxation based heuristic for the consolidation problem of airfreight forwarders

Abstract: International air cargo is an operation-intensive industry, involving complex procedures and many players. As an important player, airfreight forwarders need to consolidate the collected goods skillfully in order to satisfy the requirements of the shippers and, at the same time minimize the expense charged by the airlines. However, the air cargo rate structure is very complicated, making the consolidation a difficult mixed-integer programming problem for the airfreight forwarder. In this paper, the consolidation problem is first transformed into a well-known set covering problem by treating a feasible consolidated shipment as a set. Lagrangian Relaxation is used as the backbone to develop a recursive heuristic algorithm. Based on the numerical experiment, the heuristic algorithm generates solutions very close to optimality. In particular, a sensitivity analysis is performed with respect to the degree of concavity. The results suggest that the solution algorithm can be used as a core module of the decision support system for air cargo consolidation.

On the modeling and solution algorithm for the reverse logistics recycling flow equilibrium problem

Abstract: This paper presents a study that characterizes, formulates, and solves the reverse logistic recycling flow equilibrium (RLRFE) problem. The RLRFE problem is concerned with the recycling channel in which recyclable collectors, processors, landfills, and demand markets form a multi-tiered network to process the recycled material flows from sources destined either for landfills or demand markets. Motivated by a government policy making or enterprise conglomerate recycling system design and operation needs, the RLRFE problem is elaborated from a system-optimal perspective using the variational inequality (VI) approach. For each origin–destination (OD) pair, the corresponding equilibrium conditions are established as a variation of the Wardrop second principle. In light of demand and cost function interactions, a nested diagonalization solution (ND) algorithm is proposed that gradually transforms the RLRFE problem into a traffic assignment model. To address multiple landfills in the recycling network and to understand how a variable-demand problem can be analyzed as a fixed-demand problem, we propose a supernetwork representation of the RLRFE problem. A numerical analysis on a test case illustrates the model formulation and the proposed algorithm.

Comparison of pattern recognition techniques for the classification of impact acoustic emissions.

Abstract: Current day condition monitoring applications involving wood are mostly carried out through visual inspection and if necessary some impact acoustic examination is carried out These inspections are mainly done intuitively by skilled personnel In this paper, a pattern recognition approach has been considered to automate such intuitive human skills for the development of robust and reliable methods within the area The study presents a comparison of several pattern recognition techniques combined with various stationary feature extraction techniques for classification of impact acoustic emissions Further issues concerning feature fusion are discussed as well It is hoped that this kind of broad analysis could be used to handle a wide spectrum of tasks within the area, and would provide a perfect ground for future research directions A brief introduction to the techniques is provided for the benefit of the readers unfamiliar with the techniques Pattern classifiers such as support vector machines, etc are combined with stationary feature extraction techniques such as linear predictive cepstral coefficients, etc Results from support vector machines in combination with linear predictive cepstral coefficients delivered good classification rates However, Gaussian mixture models delivered higher classification rates when feature fusion is proposed

A dynamic network loading process with explicit delay modelling

Abstract: In this paper, a neural network (NN) approximator, integrated to a dynamic network loading (DNL) process, is utilized to model delays and to solve the DNL problem at an unsignalized highway node. First, a dynamic node model (DNM) is set out to compute the time-varying traffic flows conflicting at the node. The presented DNM has two components: a link model set with a linear travel time function and an algorithm written with a set of node rules considering the constraints of conservation, flow splitting rates and non-negativity. Each of the selected NN methods, feed-forward back-propagation NN, radial basis function NN, and generalized regression NN, are utilized one by one in the NN approximator that is integrated with the proposed DNM, and, hence, three DNL processes are simulated. Delays forming as a result of capacity constraint and flow conflicting at the node are calculated with selected NN configurations after calibrating the NN component with conical delay function formulation. The results of the model structure, run solely with the conventional delay function, are then compared to evaluate the performance of the models supported with NNs relatively.

Three-player game-theoretic model over a freight transportation network

Abstract: With recent development in freight transportation industry, its network structure has become more complicated, as many decision-makers competing for profits with each other are involved. While most recent research in this area is focused on the perfectly competitive market and the prices are given as a constant tariff rate, little attention has been paid to the system optimization problem in the absence of regulatory authority. In this paper, we investigate the competitive equilibrium in an oligopolistic market on a freight network. A partially non-cooperative game among shippers, carriers and infrastructure companies (IC) is examined. All three kinds of players act as profit maximizing agents, except that the carriers and ICs are assumed to behave cooperatively in their own coalitions. We consider the vertically efficient nonlinear tariff schedules which are commonly used in the transportation industry. By introducing a three-stage game-theoretic model, we show that the equilibrium flows can also maximize total system profits if the IC and the carrier both use vertically efficient nonlinear pricing schedules. The division of the surplus associated with each shipment is obtained by solving a linear programming problem. We provide a few examples under different situations to show the existence of the resulting equilibrium.

Coordinated scheduling models for allied airlines

Abstract: Recently, as a means of forming global networks and improving operation efficiency, major air carriers have increasingly entered into alliances with other carriers. Fleet routing and flight scheduling are not only important in individual airline operations, but also affect the alliances. The setting of a good flight schedule can not only enhance allied airline operating performance, but can also be a useful reference for alliance decision-making. In this research, we develop several coordinated scheduling models, which will help the allied airlines solve for the most satisfactory fleet routes and timetables under the alliance. We employ network flow techniques to construct the models. The models are formulated as multiple commodity network flow problems which can be solved using a mathematical programming solver. Finally, to evaluate the models, we perform a case study based on real operating data from two Taiwan airlines. The preliminary results are good, showing that the models could be useful for airline alliances.

Statistical characteristics of transitional queue conditions in signalized arterials

Abstract: The performance of signalized arterials is related to queuing phenomena. The paper investigates the effect of transitional traffic flow conditions imposed by the formation and dissipation of queues. A cross-recurrence quantification analysis combined with Bayesian augmented networks are implemented to reveal the prevailing statistical characteristics of the short-term traffic flow patterns under the effect of transitional queue conditions. Results indicate that transitions between free-flow conditions, critical queue conditions that exceed the detector's length, as well as the occurrence of spillovers impose a set of prevailing traffic flow patterns with different statistical characteristics with respect to determinism, nonlinearity, non-stationarity and laminarity. The complexity in critical queue conditions is further investigated by introducing two supplementary regions in the critical area before spillover occurrence. Results indicate that the supplementary information on the transitional conditions in the critical area increases the accuracy of the predictive relations between the statistical characteristics of traffic flow evolution and the occurrence of transitions.

Using geometric primitives to calibrate traffic scenes

Abstract: In this paper, we address the problem of recovering the intrinsic and extrinsic parameters of a camera or a group of cameras in a setting overlooking a traffic scene. Unlike many other settings, conventional camera calibration techniques are not applicable in this case. We present a method that uses certain geometric primitives commonly found in traffic scenes, such as straight and curved lanes, lane markings, and poles in order to recover calibration parameters. We show experimentally that these primitives provide the needed redundancy and are capable of achieving accurate results suitable for most traffic monitoring applications.