Tomer Toledo

Bio: Tomer Toledo is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Traffic simulation & Poison control. The author has an hindex of 38, co-authored 128 publications receiving 4391 citations. Previous affiliations of Tomer Toledo include Massachusetts Institute of Technology & Transport Research Institute.

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.

Modeling integrated lane-changing behavior

Abstract: 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.

In-vehicle data recorders for monitoring and feedback on drivers' behavior

Abstract: This paper describes the potential of in-vehicle data recorder (IVDR) systems to be used in various commercial and research applications as tools to monitor and provide feedback to drivers on their on-road behavior. The implementation of IVDR is demonstrated using the example of the DriveDiagnostics system. This system can identify various maneuver types that occur in the raw measurements, and use this information to calculate risk indices that indicate on the overall trip safety. Drivers receive feedback through various summary reports, real-time text messages or an in-vehicle display unit. Validation tests with the system demonstrate promising potential as a measurement tool to evaluate driving behavior. Reductions in crash rates and the risk indices are observed in the short-term.

Modeling Duration of Lane Changes

Abstract: Lane changes significantly affect the characteristics of traffic flow. Lane-changing models are therefore important in microscopic traffic simulation. Existing lane-changing models emphasize the decision-making aspects of the task but generally neglect the detailed modeling of the lane-changing action itself and model it only as an instantaneous event. However, research indicates that lane-changing durations are on average in the range of 5 to 6 s. The omission of lane-changing durations from simulation models may have a significant impact on simulation outputs. Models of the duration of lane changes are presented. These models are estimated by using detailed vehicle trajectory data that were collected in naturalistic driving with high-mounted video cameras. Separate models are presented for passenger cars and for heavy vehicles and statistical tests are conducted for the similarity between the lane-change durations of the two vehicle types.

General Lane-Changing Model MOBIL for Car-Following Models

Abstract: A general model (minimizing overall braking induced by lane change, MOBIL) is proposed to derive lane-changing rules for discretionary and mandatory lane changes for a wide class of car-following models. Both the utility of a given lane and the risk associated with lane changes are determined in terms of longitudinal accelerations calculated with microscopic traffic models. This determination allows for the formulation of compact and general safety and incentive criteria for both symmetric and asymmetric passing rules. Moreover, anticipative elements and the crucial influence of velocity differences of these car-following models are automatically transferred to the lane-changing rules. Although the safety criterion prevents critical lane changes and collisions, the incentive criterion takes into account the advantages and disadvantages of other drivers associated with a lane change via the "politeness factor." The parameter allows one to vary the motivation for lane changing from purely egoistic to more c...

Transportation network analysis

Abstract: Transportation Networks. Optimality. Cost Functions. Deterministic User Equilibrium Assignment. Stochastic User Equilibrium Assignment. Trip Table Estimation. Network Reliability. Network Design. Conclusions. References. Index.