Designating lanes for different vehicle types is ideal road safety-wise. Practical considerations, however, require road sharing. Using a modified Nagel-Schrekenberg cellular automata model for two vehicle types (cars and motorcycles), we analyzed the interplay of lane discipline, lane changing, and vehicle density. In the absence of lane changing, the transition between free flow and congested states occurs at a higher vehicle (road occupation) density when the ratio of cars to motorcycles is increased. When lane changing is allowed, the smaller motorcycles tend to fill in unused spaces, until the point when the wider cars effectively block their way at high vehicle densities. When the condition of lane discipline is not imposed, i.e. staying wholly within lane boundaries is not required, further improvement in throughput becomes possible at the cost of required driver attentiveness.

Lane changing and vehicular slowdowns are known to impact traffic flow. Using a modified Nagel-Schreckenberg cellular automata model for two vehicle types: blocking (e.g. cars) and non-blocking (e.g. motorcycles), we determined the thresholds at which the interplay of lane changing, random and non-random slowdowns strongly impact vehicle speeds. Lane changing improves speed with diminishing returns as vehicles opt to change lanes. At the same time, lane changing is detrimental to the overall speed when lane straddling occurs. Increasing random slowdowns beyond a critical value (in the case of motorcycles, slowdown values of p slow ≈ [0.2, 0.3, 0.4] for densities ρ = [0.20, 0.15, 0.10] respectively) can force crossover from free flowing traffic into a state where interactions between vehicles reduce the average speed.

Given school enrollments but in the absence of a student residence census, we present a gravity-like model to infer the residential distribution of enrolled students across various administrative units. Multi-scale analysis of the effects of aggregation across different administrative levels allows for the identification of administrative units with sub-optimally located schools and highlights the challenges in allocating resources. Using this method, we verify that the current scheme of free cross-enrollment across administrative boundaries is needed in achieving universal education in the Philippines.

Data-driven and machine-learning-based methods are increasingly used in attempts to master the challenges of the world. But are they really the best approaches to manage complex dynamical systems? Our aim is to gain more insights into this question by studying various popular reinforcement learning methods for traffic signal control, namely in disrupted scenarios characterized by significant, unpredictable variations. The results are expected to be relevant in subject areas ranging from traffic physics to transportation theory, from dynamics in networks to complex systems, from control theory to self-organization, and from adaptive heuristics to machine learning.

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