The area of interest (AOI) reflects the degree of attention of a driver while driving. The division of AOI is visual characteristic analysis required in both real vehicle tests and simulated driving scenarios. Some key eye tracking parameters and their transformations can only be obtained after the division of AOI. In this study, 9 experienced and 7 novice drivers participated in real vehicle driving tests. They were asked to drive along a freeway section and a highway section, wearing the Dikablis eye tracking device. On average, 8132 fixation points for each driver were extracted. After coordinate conversion, the MSAP (Mean Shift Affinity Propagation) method is proposed to classify the distribution of fixation points into a circle type and a rectangle type. Experienced drivers’ fixation behavior falls into the circle type, in which fixation points are concentrated. Novice drivers’ fixation points, which are decentralized, are illustrated in the rectangle type. In the clustering algorithm, the damping coefficient λ determines the algorithm convergence, and the deviation parameter p mainly affects the number of clusters, where larger p values generate more clusters. This study not only provides the cluster type and cluster counts, but also presents the borderlines for each cluster. The findings provide significant contribution to eye tracking research.

The rapid development of bike sharing has posed some challenges to the traffic management on campus. The bike sharing on campus has problems such as messy parking, and some buildings in the peak hours have no bikes to borrow. Thus, alternative parking spots are proposed based on the layout principle of parking spots for bicycles. An optimization model of the layout of campus bike-sharing parking spots with travel time and construction cost as the optimization goal is established, and the branch and bound algorithm is used to solve the model. Finally, the study analysis is carried out by optimizing the layout of the bike-sharing parking spot of Nanjing University of Science and Technology. The results show that, after optimizing the layout of parking spots, the average travel time of users is reduced by 6.0%, and the total construction cost is reduced by 27.3%. While being convenient for campus bike-sharing users, it also provides scientific decision-making support for the campus traffic management.

In the State of Florida, several types of exit ramps are used for traffic to exit our freeways, especially four typical types, which are single-lane exit ramp with a taper, single-lane exit ramp without a taper, two-lane exit ramp with an optional lane, and two-lane exit ramp without an optional lane. Drivers exiting freeways need to make decisions and execute maneuvers (i.e., lane change or lane merge) prior to the exit ramp in order to access cross roads at the interchanges. And different types of exit ramps lead to different driving maneuvers when exiting (fast-paced deceleration, lane changing, merging, unbalanced lane utilization), which also affects the operational performance on freeways. Bases on the facts, there are several issues and concerns that need to be addressed in selecting the most optimum types of freeway exit ramp(s) to use at a given interchange. This paper mainly focuses on different impacts of number of lane change by different types of ramp. A data collection of 24 sites were conducted to help set up a mathematical model, which could estimate the number of lane change by considering several possible independent variables. Results of the model could help choose optimal ramp type.

The use of nontraversable median cross sections and directional median openings has generated increased numbers of U-turning vehicles at signalized intersections. A procedure is needed to help transportation professionals determine the impacts of proposed median-modification projects on traffic operations at downstream signalized intersections. This study is a continuous effort following previous studies to evaluate the impacts of indirect driveway left-turn treatments on traffic operations at signalized intersections. In the present study, a total of 80 h of traffic data were collected at 66 roadway segments in the Tampa Bay area of Florida. On the basis of the data collected in the field, the research team developed a negative-binomial model to estimate the expected number of U-turning vehicles on a left-turn approach at a signalized intersection during weekday peak periods. The model has only five independent variables, and all of these variables can be easily measured in the field. By combining the research results of this study and previous studies, a procedure is provided to help transportation professionals evaluate the impacts of proposed indirect driveway left-turn treatments on traffic operations at downstream signalized intersections.