Haris N. Koutsopoulos

SIMULATION LABORATORY FOR EVALUATING DYNAMIC TRAFFIC MANAGEMENT SYSTEMS

Advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS) are promising technologies for achieving efficiency in the operation of transportation systems. A simulation-based laboratory environment, MITSIMLab, is presented that is designed for testing and evaluation of dynamic traffic management systems. The core of MITSIMLab is a microscopic traffic simulator (MITSIM) and a traffic management simulator (TMS). MITSIM represents traffic flows in the network, and the TMS represents the traffic management system under evaluation. An important feature of MITSIMLab is its ability to model ATMS or ATIS that generate traffic controls and route guidance based on predicted traffic conditions. A graphical user interface allows visualization of the simulation, including animation of vehicle movements. An ATIS case study with a realistic network is also presented to demonstrate the functionality of MITSIMLab.

IBM infosphere streams for scalable, real-time, intelligent transportation services

With the widespread adoption of location tracking technologies like GPS, the domain of intelligent transportation services has seen growing interest in the last few years. Services in this domain make use of real-time location-based data from a variety of sources, combine this data with static location-based data such as maps and points of interest databases, and provide useful information to end-users. Some of the major challenges in this domain include i) scalability, in terms of processing large volumes of real-time and static data; ii) extensibility, in terms of being able to add new kinds of analyses on the data rapidly, and iii) user interaction, in terms of being able to support different kinds of one-time and continuous queries from the end-user. In this paper, we demonstrate the use of IBM InfoSphere Streams, a scalable stream processing platform, for tackling these challenges. We describe a prototype system that generates dynamic, multi-faceted views of transportation information for the city of Stockholm, using real vehicle GPS and road-network data. The system also continuously derives current traffic statistics, and provides useful value-added information such as shortest-time routes from real-time observed and inferred traffic conditions. Our performance experiments illustrate the scalability of the system. For instance, our system can process over 120000 incoming GPS points per second, combine it with a map containing over 600,000 links, continuously generate different kinds of traffic statistics and answer user queries.

Travel time estimation for urban road networks using low frequency probe vehicle data

The paper presents a statistical model for urban road network travel time estimation using vehicle trajectories obtained from low frequency GPS probes as observations, where the vehicles typically cover multiple network links between reports. The network model separates trip travel times into link travel times and intersection delays and allows correlation between travel times on different network links based on a spatial moving average (SMA) structure. The observation model presents a way to estimate the parameters of the network model, including the correlation structure, through low frequency sampling of vehicle traces. Link-specific effects are combined with link attributes (speed limit, functional class, etc.) and trip conditions (day of week, season, weather, etc.) as explanatory variables. The approach captures the underlying factors behind spatial and temporal variations in speeds, which is useful for traffic management, planning and forecasting. The model is estimated using maximum likelihood. The model is applied in a case study for the network of Stockholm, Sweden. Link attributes and trip conditions (including recent snowfall) have significant effects on travel times and there is significant positive correlation between segments. The case study highlights the potential of using sparse probe vehicle data for monitoring the performance of the urban transport system.We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 1 s and 2 min. We introduce a new class of algorithms, which are altogether called the path inference filter (PIF), that maps GPS data in real time, for a variety of tradeoffs and scenarios and with a high throughput. Numerous prior approaches in map matching can be shown to be special cases of the PIF presented in this paper. We present an efficient procedure for automatically training the filter on new data, with or without ground-truth observations. The framework is evaluated on a large San Francisco taxi data set and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The PIF has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco and Sacramento, CA, USA; Stockholm, Sweden; and Porto, Portugal.

Modeling Integrated Lane-changing Behavior

The lane-changing model is an important component within microscopic traffic simulation tools. Following the emergence of these tools in recent years, interest in the development of more reliable lane-changing models has increased. Lane-changing behavior is also important in several other applications such as capacity analysis and safety studies. Lane-changing behavior is usually modeled in two steps: (a) the decision to consider a lane change, and (b) the decision to execute the lane change. In most models, lane changes are classified as either mandatory (MLC) or discretionary (DLC). MLC are performed when the driver must leave the current lane. DLC are performed to improve driving conditions. Gap acceptance models are used to model the execution of lane changes. The classification of lane changes as either mandatory or discretionary prohibits capturing trade-offs between these considerations. The result is a rigid behavioral structure that does not permit, for example, overtaking when mandatory considerations are active. Using these models within a microsimulator may result in unrealistic traffic flow characteristics. In addition, little empirical work has been done to rigorously estimate the parameters of lane-changing models. An integrated lane-changing model, which allows drivers to jointly consider mandatory and discretionary considerations, is presented. Parameters of the model are estimated with detailed vehicle trajectory data.

DEVELOPMENT OF A ROUTE GUIDANCE GENERATION SYSTEM FOR REAL-TIME APPLICATION

Abstract This paper describes the route guidance generation component of DynaMIT, a dynamic traffic assignment (DTA) system intended for deployment in a traffic center and capable of generating real-time prediction-based guidance information. After providing a general overview of the system, the paper discusses the principal theoretical and algorithmic issues which influenced the development of its guidance generation component.

Real-time simulation of traffic demand-supply interactions within DynaMIT

DynaMIT is a simulation-based real-time system designed to estimate the current state of a transportation network, predict future traffic conditions, and provide consistent and unbiased information to travelers. To perform these tasks, efficient simulators have been designed to explicitly capture the interactions between transportation demand and supply. The demand reflects both the OD flow patterns and the combination of all the individual decisions of travelers while the supply reflects the transportation network in terms of infrastructure, traffic flow and traffic control. This paper describes the design and specification of these simulators, and discusses their interactions.

Hybrid Mesoscopic-Microscopic Traffic Simulation

Traffic simulation is an important tool for modeling the operations of dynamic traffic systems. Although microscopic simulation models provide a detailed representation of the traffic process, macroscopic and mesoscopic models capture the traffic dynamics of large networks in less detail but without the problems of application and calibration of microscopic models. This paper presents a hybrid mesoscopic-microscopic model that applies microscopic simulation to areas of specific interest while simulating a large surrounding network in less detail with a mesoscopic model. The requirements that are important for a hybrid model to be consistent across the models at different levels of detail are identified. These requirements vary from the network and route choice consistency to the consistency of the traffic dynamics at the boundaries of the microscopic and mesoscopic submodels. An integration framework that satisfies these requirements is proposed. A prototype hybrid model is used to demonstrate the application of the integration framework and the solution of the various integration issues. The hybrid model integrates MlTSIMLab, a microscopic traffic simulation model, and Mezzo, a newly developed mesoscopic model. The hybrid model is applied in two case studies. The results are promising and support both the proposed architecture and the importance of integrating microscopic and mesoscopic models.

Non-linear kalman filtering algorithms for on-line calibration of dynamic traffic assignment models

The problem of on-line calibration of dynamic traffic assignment (DTA) models is receiving increasing attention from researchers and practitioners. The problem can be formulated as a non-linear state-space model. Because of its nonlinear nature, the resulting model cannot be solved by the Kalman filter and therefore non-linear extensions need to be considered. In this paper, three extensions to the Kalman filter algorithm are presented: extended Kalman filter (EKF), limiting EKF (LimEKF), and unscented Kalman filter (UKF). The solution algorithms are applied to the calibration of the state-of-the-art DynaMIT-R DTA model and their use is demonstrated in a freeway network in Southampton, U.K. The LimEKF shows accuracy comparable to that of the best algorithm, but vastly superior computational performance

CALIBRATION AND VALIDATION OF MICROSCOPIC TRAFFIC SIMULATION TOOLS: STOCKHOLM CASE STUDY

The calibration and validation approach and results from a case study applying the microscopic traffic simulation tool MITSIMLab to a mixed urban-freeway network in the Brunnsviken area in the north of Stockholm, Sweden, under congested traffic conditions are described. Two important components of the simulator were calibrated: driving behavior models and travel behavior components, including origin–destination flows and the route choice model. In the absence of detailed data, only aggregate data (i.e., speed and flow measurements at sensor locations) were available for calibration. Aggregate calibration uses simulation output, which is a result of the interaction among all components of the simulator. Therefore, it is, in general, impossible to identify the effect of individual models on traffic flow when using aggregate data. The calibration approach used takes these interactions into account by iteratively calibrating the different components to minimize the deviation between observed and simulated measurements. The calibrated MITSIMLab model was validated by comparing observed and simulated measurements: traffic flows at sensor locations, point-to-point travel times, and queue lengths. A second set of measurements, taken a year after the ones used for calibration, was used at this stage. Results of the validation are presented. Practical difficulties and limitations that may arise with application of the calibration and validation approach are discussed.

TRAVEL SIMULATORS FOR DATA COLLECTION ON DRIVER BEHAVIOR IN THE PRESENCE OF INFORMATION

Abstract Understanding traveler response to potential ATIS services is critical for designing such services and evaluating their effectiveness. Extensive data is required for developing the models necessary to provide this understanding. In this paper we examine one source of such data: traveler simulators. We make a distinction between travel simulators, used to study the travelers response to information acquisition, and driving simulators, which are elaborate tools used mainly for human factors research. Traveler simulators have the potential to provide a wealth of data collected relatively inexpensively under controlled conditions. However the data may suffer from biases introduced because of the laboratory nature of travel simulators. We examine various existing simulators and comment on their advantages and disadvantages. We make recommendations for simulator design characteristics that increase the reliability of the data collected and suggest enhancements so that current simulators can be used for the collection of data related to access and acquisition of ATIS products as well. We conclude the paper with recommendations for future research in the area.