Lane-merge optimization - Revision history

Martin.zima: typo

2016-11-03T22:06:10Z

typo

Martin.zima: added reference

2016-01-17T22:59:30Z

added reference

Martin.zima: added reference

2016-01-17T22:55:40Z

added reference

Martin.zima: added source code

2016-01-17T22:45:17Z

added source code

Martin.zima: Results

2016-01-17T22:41:36Z

Results

Martin.zima: work in progress: Parameters, Results

2016-01-17T22:15:15Z

work in progress: Parameters, Results

Martin.zima: work in progress: Results, Conclusion

2016-01-17T21:28:33Z

work in progress: Results, Conclusion

Martin.zima: work in progress: Parameters, Results

2016-01-17T19:35:07Z

work in progress: Parameters, Results

Martin.zima: missing comma

2016-01-17T17:21:39Z

missing comma

Martin.zima: phrasing, typos

2016-01-17T17:20:48Z

phrasing, typos

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 During the past centuries, once technologies became advanced enough to start the industrial revolution, 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 of its interacting agents and their nature.
-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.
+In comparison with other means of transport, car traffic has many peculiarities of its own: for starters, there is usually a significantly higher number 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. The reasons for a lane-merge are numerous - ranging from a simple street narrowing to various traffic obstructions (car accident, roadworks...). Due to its nature (lower number of lanes will always lead to theoretically lower maximum throughput), but also possibly due to human error, different agents' driving 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 <ref>Traffic Control Systems Handbook: Chapter 4. Control and Management Concepts For Freeways. US Department of Transporation: Federal Highway Administration [online]. [cit. 2016-01-17]. Available at: [http://ops.fhwa.dot.gov/publications/fhwahop06006/chapter_4.htm]</ref>. 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.

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 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. The reasons for a lane-merge are numerous - ranging from a simple street narrowing to various traffic obstructions (car accident, roadworks...). Due to its nature (lower number of lanes will always lead to theoretically lower maximum throughput), but also possibly due to human error, different agents' driving 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.
+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. The reasons for a lane-merge are numerous - ranging from a simple street narrowing to various traffic obstructions (car accident, roadworks...). Due to its nature (lower number of lanes will always lead to theoretically lower maximum throughput), but also possibly due to human error, different agents' driving 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 <ref>Traffic Control Systems Handbook: Chapter 4. Control and Management Concepts For Freeways. US Department of Transporation: Federal Highway Administration [online]. [cit. 2016-01-17]. Available at: [http://ops.fhwa.dot.gov/publications/fhwahop06006/chapter_4.htm]</ref>. 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 ==

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 Amongst the obvious factors that generally affect the whole lane-merge performance in a positive way is the speed of the cars and their acceleration. These affect the absolute numbers of throughput, but in real-life, it is often not feasible to improve these parameters (environment limitations, safety, etc.). On a side note, while a higher average speed will usually also mean a higher throughput, it may not always be that case if the speed differences among the cars are too high and their acceleration is not sufficiently fast to cover up losses. This may result in congestions even in light traffic conditions as the slowed-down cars are slowing down in a chain reaction, queuing and then only slowly recovering from this decceleration.
-Conversely, the safety gaps between cars proved to be inversely proportional to the total throughput. However, it is important to note that their real impact might still be a little bit different because of the braking overreaction phenomenon we omitted from this simulation (as some different studies show, adequately-sized distances between cars might actually mitigate the braking overreaction).
+Conversely, the safety gaps between cars proved to be inversely proportional to the total throughput. However, it is important to note that their real impact might still be a little bit different because of the braking overreaction phenomenon we omitted from this simulation (as some different studies show <ref>ZHAO, Yi. PREDICTING TRAFFIC CONGESTION WITH DRIVING BEHAVIOR [online]. [2016-01-17]. Available at: [https://www.geotab.com/geoimages/blog/download/traffic-congestion.pdf]</ref>, adequately-sized distances between cars might actually mitigate the braking overreaction).
 == Conclusion ==
 This paper presented some of the basic phenomena that may be witnessed during lane-merging and evaluated some of the possible factors causing them. Using this data and created simulation, we could optimize the lane-merging process of autonomously-driven cars, or confirm if new traffic rules are not hindering the traffic efficiency. It might also be possible to follow-up with the study of this problem and develop a more sophisticated simulation solution, better corresponding to the real-world situations (e.g. the omitted braking overreaction problem).
 == Code ==

← Older revision		Revision as of 22:45, 17 January 2016
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	== Code ==		== Code ==
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		+	NetLogo model file:
		+	[[File:Lane_merge_v100_zima.nlogo]]

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 As it turns out, the overtaking inclination (how many drivers switch lanes in an effort to get ahead of the slower cars) has very little to none effect on the result of the simulation.
 === Willingness to let another car zip-merge ===
-As discussed in the chapter describing behavior of agents, this parameter represents the percentage of cars which slow-down to let another car switch the lanes at a lane merge. According to expectations, it affects the overall performance of the lane merge, but does not necessarily improve the total throughput. The most evident effect of decreasing the willingness of agents in one (merged-to) lane is rapid slowing-down of agents merging from the other, leading up to creating long waiting queues in that lane. This may, depending on the traffic density, in the lane that is being merged-to, result or not result in lowering the total throughput (if there is already enough cars in the lane that the waiting agents want to switch to, it does not affect the throughput at all).
+As discussed in the chapter describing behavior of agents, this parameter represents the percentage of cars which slow-down to let another car switch the lanes at a lane merge. According to expectations, it affects the overall performance of the lane merge, but does not necessarily improve the total throughput. The most evident effect of decreasing the willingness of agents in one (merged-to) lane is rapid slowing-down of agents merging from the other, leading up to creating long waiting queues in that lane. This may, depending on the traffic density, in the lane that is being merged-to, result or not result in lowering the total throughput (if there is already enough cars in the lane that the waiting agents want to switch to, it does not affect the throughput at all). [[File:Lane mergers43 speeddifferences interface small.png|right|thumb|x150px|Even though the traffic isn't heavy, the queue is growing.]]
 === Speed, acceleration, safety gaps ===
 Amongst the obvious factors that generally affect the whole lane-merge performance in a positive way is the speed of the cars and their acceleration. These affect the absolute numbers of throughput, but in real-life, it is often not feasible to improve these parameters (environment limitations, safety, etc.). On a side note, while a higher average speed will usually also mean a higher throughput, it may not always be that case if the speed differences among the cars are too high and their acceleration is not sufficiently fast to cover up losses. This may result in congestions even in light traffic conditions as the slowed-down cars are slowing down in a chain reaction, queuing and then only slowly recovering from this decceleration.

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 The objective of this paper was to primarily simulate the natural behavior of human agents driving cars. To achieve this, the model tries to mimic number of aspects of the typical human driver behavior. First of all, because our goal is not to simulate car crash accidents (we simply assume no agent makes such a crude error - otherwise we would be simulating a completely different thing), the priority number one of every agent is maintaining a safe speed and distance between cars (i.e. him and the possible car ahead of him). If circumstances allow (environment and other surrounding agents), the agent will also then accelerate up to his desired speed. For the simplicity of this simulation and also to reduce possible undesired effects, we assume the agents have an immediate reaction while braking and are able to decelerate to zero instantly (which better corresponds to lower-speed situations, like with lane merges in cities) - it was chosen not to include the braking overreaction in this simulation. This is unlike the positive acceleration (increasing), which is simulated to take time. The acceleration time also corresponds to a possible reaction time of the considered agent.
-Moreover, unless there is a lane merger nearby (which would prevent him from doing it), he may also overtake a slower-driving agent in front of him, using the lane to the left (if there is one). Once he finishes this maneuver, he pulls back to his original lane (and eventually any other lane to the right of him as long as there is enough free space and he reached his desired speed). Presented simulation better corresponds to the situation of a city street road than other multi-lane roads (like freeways), because it allows a faster agent to overtake another agent also using the lane on the right of him (which is usually forbidden in many countries), as long he was in that lane first.
+Moreover, unless there is a lane merger nearby (which would prevent him from doing it), he may also overtake a slower-driving agent in front of him, using the lane to the left (if there is one). Once he finishes this maneuver, he might (parameterized) pull back to his original lane (and eventually any other lane to the right of him as long as there is enough free space and he reached his desired speed). Presented simulation better corresponds to the situation of a city street road than other multi-lane roads (like freeways), because it allows a faster agent to overtake another agent also using the lane on the right of him (which is usually forbidden in many countries), as long he was in that lane first.
 Agent switches to a slightly different behavior once he approaches a line merge (the point, where a lane ends, and he is forced to switch the lane). From certain distance to the lane end, the agent starts to check if there enough space in the lane he wants to merge to, and in case there is, then if he is the last one in his lane or if he prefers an early merge, he starts to switch the lane. If the agent does not succeeds with this, then, after another certain distance-to-lane-end value, he starts to 'worry' he might not make it in time, resulting in a sudden deceleration to lower speed. During this period, if there happens to be another agent to his right willing to let him merge ('zip'), the agent to the right slows down and lets him merge. If he does not succeed with this either, he might eventually get to a point, where he is forced to stop the car completely (speed set to zero).
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 * Inclination to overtake slower drivers (%)
 * Min/max range for safe distance between cars required by an agent
 == Results ==
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 === Speed, acceleration, safety gaps ===
-Amongst the obvious factors that generally affect the whole lane-merge performance in a positive way is the speed of the cars and their acceleration. On the other hand, the safety gaps between cars proved to be inversely proportional to the total throughput. However, it is important to note that their real impact might still be a little bit different because of the braking overreaction phenomenon we omitted from this simulation (as some different studies show, adequately-sized distances between cars might actually mitigate the braking overreaction).
+[[File:Lane mergers43 speeddifferences interface small.png|right|thumb|x403px|Even though the traffic isn't heavy, the queue is growing.]]
 == Conclusion ==

@@ Line 30: / Line 30: @@
 === Behavior of agents ===
-The objective of this paper was to primarily simulate the natural behavior of human agents driving cars. To achieve this, the model tries to mimic number of aspects of the typical human driver behavior. First of all, because our goal is not to simulate car crash accidents (we simply assume no agent makes such a crude error - otherwise we would be simulating a completely different thing), the priority number one of every agent is maintaining a safe speed and distance between cars (i.e. him and the possible car ahead of him). If circumstances allow (environment and other surrounding agents), the agent will also then accelerate up to his desired speed. For the simplicity of this simulation and also to reduce possible undesired effects, we assume the agents have an immediate reaction while braking and are able to decelerate to zero instantly (unlike the positive acceleration, which takes time).
+The objective of this paper was to primarily simulate the natural behavior of human agents driving cars. To achieve this, the model tries to mimic number of aspects of the typical human driver behavior. First of all, because our goal is not to simulate car crash accidents (we simply assume no agent makes such a crude error - otherwise we would be simulating a completely different thing), the priority number one of every agent is maintaining a safe speed and distance between cars (i.e. him and the possible car ahead of him). If circumstances allow (environment and other surrounding agents), the agent will also then accelerate up to his desired speed. For the simplicity of this simulation and also to reduce possible undesired effects, we assume the agents have an immediate reaction while braking and are able to decelerate to zero instantly (which better corresponds to lower-speed situations, like with lane merges in cities) - it was chosen not to include the braking overreaction in this simulation. This is unlike the positive acceleration (increasing), which is simulated to take time. The acceleration time also corresponds to a possible reaction time of the considered agent.
-Moreover, unless there is a lane merger nearby (which would prevent him from doing it), he may also overtake a slower-driving agent in front of him, using the lane to the left (if there is one). Once he finishes this maneuver, he pulls back to his original lane (and eventually any other lane to the right of him as long as there is enough free space and he reached his desired speed). Presented simulation better corresponds to the situation of a city street road than other multi-lane roads (like highways), because it allows a faster agent to overtake another agent also using the lane on the right of him (which is usually forbidden in many countries), as long he was in that lane first.
+Moreover, unless there is a lane merger nearby (which would prevent him from doing it), he may also overtake a slower-driving agent in front of him, using the lane to the left (if there is one). Once he finishes this maneuver, he pulls back to his original lane (and eventually any other lane to the right of him as long as there is enough free space and he reached his desired speed). Presented simulation better corresponds to the situation of a city street road than other multi-lane roads (like freeways), because it allows a faster agent to overtake another agent also using the lane on the right of him (which is usually forbidden in many countries), as long he was in that lane first.
 Agent switches to a slightly different behavior once he approaches a line merge (the point, where a lane ends, and he is forced to switch the lane). From certain distance to the lane end, the agent starts to check if there enough space in the lane he wants to merge to, and in case there is, then if he is the last one in his lane or if he prefers an early merge, he starts to switch the lane. If the agent does not succeeds with this, then, after another certain distance-to-lane-end value, he starts to 'worry' he might not make it in time, resulting in a sudden deceleration to lower speed. During this period, if there happens to be another agent to his right willing to let him merge ('zip'), the agent to the right slows down and lets him merge. If he does not succeed with this either, he might eventually get to a point, where he is forced to stop the car completely (speed set to zero).
@@ Line 48: / Line 48: @@
 * Min/max range for acceleration (this covers both technical possibilities of a cars and agents' reaction time)
 * Early-merge method inclination (may merge sooner than cars in front of him, %)
-* Willingness to let another car zip-merge (%)
+* Willing to let another car zip-merge inclination (%)
 * Min/max range for distance to lane end at which agent ideally wants to merge
 * Min/max range for distance at which agent starts to slow-down if unable to merge
@@ Line 62: / Line 62: @@
 === Merge distances, early merge ===
-One of the most noticeable factors affecting the final throughput turned out to be the merging distances, i.e. the distance to lane-end at which an agent stars to merge (and eventually might stop to do so, obstructing the traffic behind him). This is quite easy mistake we may commonly see in the real-life: individual cars merging and slowing-down the traffic at different places lead to under-utilizing the final (right) lane, while on the other hand, the leftmost lanes may get completely hindered or start creating extensively prolonged waiting queues. The difference between this setup and setup where all cars merge at the same point at the end of the lane '''amounted up to 20% in our simulation case'''. Small remedy to this situation is if we enable an early merge (this means that the cars waiting in queue may take advantage of an opportunity to merge earlier than the cars waiting in the queue ahead of them).
+One of the most noticeable factors affecting the final throughput turned out to be the merging distances, i.e. the distance to lane-end at which an agent stars to merge (and eventually might stop to do so, obstructing the traffic behind him). This is quite easy mistake we may commonly see in the real-life: individual cars merging and slowing-down the traffic at different places lead to under-utilizing the final (right) lane, while on the other hand, the leftmost lanes may get completely hindered or start creating extensively prolonged waiting queues. The difference between this setup and setup where all cars merge at the same point at the end of the lane '''amounted up to 20% in our simulation case'''. The enabling of early merging (meaning the cars waiting in queue may take advantage of an opportunity to merge earlier than the cars waiting in the queue ahead of them) turned out to be a small remedy slightly improving this situation.
 <gallery>
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 File:Lane mergers41 merge distances A early interface.png|Cars merging at different distances, early merge enabled
 </gallery>
 == Conclusion ==
 == Code ==

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 == Preface ==
-During the past centuries, once technologies became advanced enough to start the industrial revolution, 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 of its interacting agents and their nature.
+During the past centuries, once technologies became advanced enough to start the industrial revolution, 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 of its interacting agents and their nature.
 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.