Attaining efficient road traffic through optimizing travel time and energy consumption without compromising safety is a key goal in transport telematics. Lane-changes and merging have a vital role in modelling on-ramp and off-ramp bottlenecks. Near on-ramps and off-ramps, they are often major factors to cause safety hazards and traffic breakdowns. In this paper, we address the problem of improving/optimizing merging time at on-ramps and thus reducing the merging bottlenecks. In order to merge two streams of communicationand sensor-enabled vehicles, we define a merging problem and propose a proactive optimal merging strategy that dissociates the point of decision-making from the actual merging point. Our algorithm computes the optimal merging order for the group of vehicles of the two streams. Our optimal merging algorithm for on-ramp vehicles (single-lane scenario) outperforms an efficient, previously suggested strategy as well as the conventional mainstream priority merging in terms of merging time and rate, waiting time, energy consumption, flow and average velocity (especially at the point after the initial merging point) at the cost of slightly increased average trip time for the mainstream vehicles compared to the conventional merging. We also highlight important directions for further research.

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Optimizing travel time and energy consumption without compromising safety to attain efficient road traffic is a key goal in transport telematics. Microscopic traffic simulations are important tools to study the impact of new algorithms on road traffic. The aim of these simulations is to achieve a high degree of realism through the use of microscopic car-following models, which characterize real-time interaction among individual vehicles. These models play a vital role in Advanced Vehicle Control and Safety Systems (AVCSS) such as collision warning, adaptive cruise control, or lane guidance and in modelling simulation of safety studies and capacity analysis in transportation science. Although a range of models have been proposed to model the longitudinal interaction between adjacent vehicles due to its importance, surprisingly few comparative evaluations of the models exist. In this paper, we first identify limitations of the prominent car-following models. We then propose a k-leader fuel-efficient car-following model and show that our model is effective in terms of safety, trip times, flow and fuel efficiency. We also highlight new research challenges and important directions for further research.

Given a connected graph G = (V, E), a set Vr ⊆ V of r special vertices, four distinct base vertices u 1, u2, u3, u4 ∈ V and four natural numbers r 1, r2, r3, r4 such that 4 j=1 rj = r, we wish to find a partition V 1, V2, V3, V4 of V such that Vi contains u i and ri vertices from Vr, and Vi induces a connected subgraph of G for each i, 1 ≤ i ≤ 4. We call a vertex in V r a resource vertex and the problem above of partitioning vertices of G as the resource four-partitioning problem. In this paper, we give a linear algorithm for finding a resource four-partition of a fourconnected planar graph G with base vertices located on the same face of a planar embedding. Our algortihm is based on a 4-canonical decomposition and st-numbering of G.