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.

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.

Driving behaviour models capture drivers’ tactical manoeuvring decisions in different traffic conditions. These models are essential to microscopic traffic simulation systems. The paper reviews the state‐of‐the‐art in the main areas of driving behaviour research: acceleration, lane changing and gap acceptance. Overall, the main limitation of current models is that in many cases they do not adequately capture the sophistication of drivers: they do not capture the interdependencies among the decisions made by the same drivers over time and across decision dimensions; they represent instantaneous decision‐making, which fails to capture drivers’ planning and anticipation capabilities; and only capture myopic considerations that do not account for extended driving goals and considerations. Furthermore, most models proposed in the literature were not estimated rigorously. In many cases, this is due to the limited availability of detailed trajectory data, which are required for estimation. Hence, data availability poses a significant obstacle to the advancement of driving behaviour modelling.

Models of driving behavior (e.g., car following and lane changing) describe the longitudinal and lateral movements of vehicles in the traffic stream. Calibration and validation of these models require detailed vehicle trajectory data. Trajectory data about traffic in cities in the developing world are not publicly available. These cities are characterized by a heterogeneous mix of vehicle types and by a lack of lane discipline. This paper reports on an effort to create a data set of vehicle trajectory data in mixed traffic and on the first results of analysis of these data. The data were collected through video photography in an urban midblock road section in Chennai, India. The trajectory data were extracted from the video sequences with specialized software, and the locally weighted regression method was used to process the data to reduce measurement errors and obtain continuous position, speed, and acceleration functions. The collected data were freely available at http://toledo.net.technion.ac.il/downloads . The traffic flow characteristics of these trajectories, such as speed, acceleration and deceleration, and longitudinal spacing, were investigated. The results show statistically significant differences between the various vehicle types in travel speeds, accelerations, distance keeping, and selection of lateral positions on the roadway. The results further indicate that vehicles, particularly motorcycles, move substantially in the lateral direction and that in a substantial fraction of the observations, drivers are not strictly following their leaders. The results suggest directions for development of a driving behavior model for mixed traffic streams.