Kinematic analysis of speed transitions within walking in younger and older adults.

The ability to adapt to environmental and task demands while walking is critical to independent mobility outside the home and this ability wanes with age. Such adaptability requires individuals to acutely change their walking speed. Regardless of age, changes between walking speeds are common in daily life, and are a frequent type of walking adaptability. Here, we report on older and younger adults when transitioning from preferred walking speed overground to either slower or faster walking. Specifically, we evaluated biomechanical parameters prior to, during, and post transition. Individuals approached the walking speed transition similarly, independent of whether the transition was to slower or faster walking. Regardless of age or walking speed, the step during which a walking speed transition occurred was distinct from those prior- and post- transition, with on average 0.15 m shorter step lengths, 3.6° more hip flexion, and 3.3° more dorsiflexion during stance. We also found that peak hip flexion occurred 22% later, and peak hip extension (39%), knee flexion (26%), and dorsiflexion (44%) occurred earlier in stance for both typical to slower and typical to faster walking. Older adults had altered timing of peak joint angles compared with younger adults across both acceleration and deceleration conditions, indicating age-dependent responses to changing walking speed. Our findings are an important first step in establishing values for kinematics during walking speed transitions in younger and typical older adults.

Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices.

Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology development that supports Connected Vehicles (CVs), which act as an active mobile sensor for collecting traffic data, provides an opportunity to harness the large datasets to develop novel methods regarding traffic bottlenecks detection. This paper presents a speed transition matrix based model for bottleneck probability estimation on motorways. The method is based on the computation of the speed at the vehicle transition point between consecutive motorway segments, which forms a traffic pattern that is represented using transition matrices. The main feature extracted from the traffic patterns was the center of mass, whose position is used as an input to the fuzzy-based system for bottleneck probability estimation. The proposed method is evaluated on four different simulated motorway traffic scenarios: (i) traffic collision site, (ii) short recurring bottleneck, (iii) long recurring bottleneck, and (iv) moving bottleneck. The method achieves comparable bottleneck detection results on every scenario, with a total accuracy of 92% on the validation dataset. The results indicate possible implementation of the method in the motorway traffic environment with a high CVs penetration rate using them as the sensory input data for the control systems based on the machine learning algorithms.