Lane-merge optimization

• Project name: Lane-merge optimization
• Course: 4IT496 Simulation of Systems course at University of Economics, Prague
• Author: Martin Zima (xzimm000, mailATzimamartinDOTcz)
• Model type: Agent-based simulation
• Software used: NetLogo 5.3

The model presented by this paper is simulating the situation of car multi-lane merging.

Preface

When an industrial boom started in the past centuries started, a new industry also emerged as a side-effect of its impact: the importance of transportation became obvious. Companies need efficient means of transport for their manufacturing logistics just as much as regular passengers for their every-day travels do. Managing an efficient traffic flow has always been a challenging task ever since these days, as it encompasses lots of aspects and requirements: it is necessary to provide a performant (efficient), reliable, safe and sustainable traffic flow control and figuring-out the best rules or configuration, let alone ensuring the adherence to them, has not ever been an easy task. One of the most complicated transportation types for this has been the road (car) traffic - not only for its limitation regarding the spatial dispositions (roads vs. air, sea, etc.), but also for the amount and nature of its agents.

In comparison with other means of transport, car traffic has many peculiarities of its own: for starters, there is usually a significantly higher of agents interacting at one moment a place on the road. Furthermore, the agents are usually not trained specialists: the usual requirement for their involvement in the system is the possession of a driver's license, which guarantees only an obligatory minimum experience and knowledge of the traffic rules. As any other human beings, these agents are even more prone to making mistakes while driving and making bad decisions, leading to even worse performing traffic (and finally, to situations commonly dubbed as traffic jams or congestions). There is a number of specific critical scenarios, where determining the optimal traffic control becomes even harder or is depending on so many external parameters and influences that we cannot predict a single, simple general rule, that would be best for the agents to follow. On the other hand, technology has become nowadays so advanced, there are already many ongoing projects analyzing and developing smarter computer-based systems, that could save resources (transportation costs, time...) and moreover, actually relief the traffic. Self-driven autonomous cars or interconnected smart traffic signalization are just a few of the examples of optimizations we might be hoping for in the near future.

This is where comes an opportunity for this paper - to be able to implement such optimizations, we first must know at what areas of the problem to focus on (there is this saying, 'premature optimization is the root of evil'...). One of typical examples of the aforementioned critical road traffic scenarios is the lane-merge. In simple words, a lane-merge is a situation, where two or more lanes (of one direction) merge into one. Due to its nature (lower number of lanes will always lead to theoretically lower maximum throughput), but also possibly human error, different opinions, inexperience and perhaps also different law regulations (generally, sub-optimal behavior of agents), lane-merges tend to be one of the most typical traffic jams causes. Because of the multitude of possible factors, it is almost impossible to analyze this in a mathematical (hand-computation) fashion, but is quite well suited for a computer-assisted simulation.

Problem definition

The primary goal of this paper is to create a plausible real-world model of car lane merges to study and determine factors and circumstances that influence the continuity of traffic flow and cause traffic jams, and in consequence how to avoid them, and to determine the best settings for an optimized traffic performance. Primarily, the model should take into account the common inefficiencies of human agents (human error...) and also offer another solution utilizing agents that act in a more consistent, rule-coherent, autonomously-driven-, computer-based-car-alike way. To verify and deliver the results of such model, a computer-based simulation will be developed. Based on the results of this simulation, we should be able to compare the different approaches and configurations and to measure (at least in orders of magnitude) their impact on the traffic fluency. The paper should conclude how observed phenomena affect the car traffic and whether it might be worth to enforce certain rules for the lane merges (or to implement them in the future autonomous computer-based agents).