Difference: GameTheoryTraffic (20 vs. 21)

Revision 212007-10-16 - XiAliceGao

Line: 1 to 1

META TOPICPARENT	name="WebHome"

-- ErikZawadzki - 07 Sep 2007

Line: 66 to 66

A Distributed Approach for Coordination of Traffic Signal Agents	Ana L. C. Bazzan	Multiple intersections	Maximize throughput and traffic saftey; minimize travel times and environmental costs	Pseudo-agent multiagent learning approach	Some good cites. The multiagent learning seems a little hacky
Game Theory: Potential Applications in Transportation Planning	Karim A. S. Ismail	Multiple intersections	Finding out applications for game theory in traffic	Driver timing, Road Pricing, Traffic Assignment, Public Transport, Signal Timing	Looks like a good start
Traffic adaptive control of a single intersection: A taxonomy of approaches	R.T. van Katwijk, B. De Schutter, and J. Hellendoorn	Single Intersection	Minimize the delay experienced by vehicles through manipulation of the traffic signal timings.	Various, mostly MDP-type frameworks. Dynamic programming and tree-search used to optimize	Good overview of adaptive controllers, but Porche & Lafortune (1997) is a better written overview

Deleted:

<
<

| [[http://paginas.fe.up.pt/~eol/PUBLICATIONS/2006/Eumas06_ITSUMO_final.pdf][Reinforcement Learning-based Control of Traffic Lights in Non-stationary Environments]] | Oliveira et al. | 9 intersection grid | Optimize light policy | Model with cellular automata, use RL learning|

Multi-Agent Reinforcement Learning for Traffic Control	Marco Wiering	2x3 intersection	Optimize Traffic lights	Simlulate with discrete simulation; use RL
Effects of Co-Evolution in a Complex Traffic Network	Bazzan et al.	6x6 grid	Optimize lighting control	Look at Greedy or Adaptive drivers, Greedy, Adaptive, or Q-Learning lights.	Small slice of agent, relatively few iterations allowed + no burn-in

Added:

>
>

Reinforcement Learning-based Control of Traffic Lights in Non-stationary Environments

Oliveira et al

9 intersection grid

Optimize light policy

Model with cellular automata, use RL learning

Other

Title	Author	Scope	Problem	Solution	Comments

Line: 83 to 82

Traffic Calming in Three European Cities: Recent Experience	Andrew Nash	Three European Cities	n/a	n/a	A laundry list of how Zurich, Vienna, and Munich have dealt with traffic calming
Learning Cooperative Lane Selection Strategies for Highways	D. Moriarty and P Langley	Single highway	How should 'smart cars' switch lanes in a highway?	Evolutionary algorithms	Strange problem setting (a device that tells you what lane you should be in, once you give it a speed preference?), strange solution
Implementation of the OPAC adaptive control strategy in a traffic signal network	Nathan H. Gartner et al.	Multiple intersection	Minimize delays and stops	Rolling horizon tree-search approach, which synchronization layer	Seems like a good approach, similar to ALLONS-D in goals and motivations. Gartner has other publications, and seems to cite few other people than himself... draw your own conclusions :)

Changed:

<
<

[http://delivery.acm.org/10.1145/310000/301193/p198-rogers.pdf?key1=301193&key2=1550070911&coll=GUIDE&dl=GUIDE&CFID=626325&CFTOKEN=63227024][An Adaptive Interactive Agent for Route Advice]]

S Rogers, C Fiechter, P Langley

route advice system

How to build an adaptive interactive agent to generate route advice based on user preferences

Agent generates route choices and update user model by observing feedback from user, assign costs to attributes of roads (travel time, length, road type)

pretty simple approach, using some kind of learning/optimization, possible future work include better learning algorithm + taking into account of dynamic attributes (current road conditions)

>
>

An Adaptive Interactive Agent for Route Advice

S Rogers, C Fiechter, P Langley

route advice system

How to build an adaptive interactive agent to generate route advice based on user preferences

Agent generates route choices and update user model by observing feedback from user, assign costs to attributes of roads (travel time, length, road type)

pretty simple approach, using some kind of learning/optimization, possible future work include better learning algorithm + taking into account of dynamic attributes (current road conditions)

Title	Author	Scope	Problem	Solution	Comments
A Collaborative Driving System Based on Multiagent Modelling and Simulations	S Halle, B Chaib-draa	some automated cars on a straight, one way, two lanes, highway segment	which are good coordination models for collaborative driving system (platoons of collaborating vehicles)	compared to centralized model, the multiagent teamwork model is more safe and flexible, but requires more messages to be communicated.	part of the Automobile of the 21st Century (Auto21) supported by Government of Canada, interesting idea, try to tackle traffic from driving perspective instead of from redesigning traffic facilities
The Network Effects of Alternative Road Pricing Systems	A D May et al	network analysis done for city of Cambridge and York	which 4 charging systems perform better in terms of its positive impact on traffic distribution	network analysis produced different results than conceptual analysis, possible future work include incorporating dynamic route guidance so that drivers are aware of the possible charges in advance	good facts about realistic charging systems for road use - make me wonder how highway 407 in Toronto does the charging.

View topic | History: r21 < r20 < r19 < r18 | More topic actions...