Game Model of Lane Changing for Bus Exiting at Bus Bay StopsSource: Journal of Transportation Engineering, Part A: Systems:;2021:;Volume ( 147 ):;issue: 009::page 04021046-1DOI: 10.1061/JTEPBS.0000566Publisher: ASCE
Abstract: To improve public transportation operations, this research investigates the lane-changing behaviors for buses exiting at bus bay stops. Game theory is utilized to analyze the interactions between bus and social vehicle drivers. A two-player noncooperative, non-zero-sum mixed-strategy game model is formulated to consider the time saving and collision avoidance payoffs for both players in the game. The characteristic of mass passengers carried by buses is also taken into account in the payoffs. The maximum likelihood estimation is used to calibrate the formulated model using the vehicle trajectory data collected from Lvboqiao bus bay stop, Dalian, China. The validation results reveal that the formulated model can effectively predict the lane-changing decisions with a high level of accuracy. Besides mixed strategies, the players in the game may have dominant strategies, which are the best choices that have nothing to do with the strategies that other players may adopt. In both mixed and dominant strategies, bus drivers are more inclined to choose the strategy of not changing a lane, and social vehicle drivers tend to choose the strategy of not giving way to the bus. The tendencies to choose strategies by both players are consistent with the actual decisions made by drivers. It is proven that the formulated game model is highly reliable to describe bus drivers’ exiting behaviors at bus bay stops and can offer the decent theoretical support for both players to make better decisions.
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contributor author | Ronghan Yao | |
contributor author | Xiaojing Du | |
contributor author | Wensong Zhang | |
contributor author | He Zhang | |
date accessioned | 2022-02-01T21:42:13Z | |
date available | 2022-02-01T21:42:13Z | |
date issued | 9/1/2021 | |
identifier other | JTEPBS.0000566.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4271873 | |
description abstract | To improve public transportation operations, this research investigates the lane-changing behaviors for buses exiting at bus bay stops. Game theory is utilized to analyze the interactions between bus and social vehicle drivers. A two-player noncooperative, non-zero-sum mixed-strategy game model is formulated to consider the time saving and collision avoidance payoffs for both players in the game. The characteristic of mass passengers carried by buses is also taken into account in the payoffs. The maximum likelihood estimation is used to calibrate the formulated model using the vehicle trajectory data collected from Lvboqiao bus bay stop, Dalian, China. The validation results reveal that the formulated model can effectively predict the lane-changing decisions with a high level of accuracy. Besides mixed strategies, the players in the game may have dominant strategies, which are the best choices that have nothing to do with the strategies that other players may adopt. In both mixed and dominant strategies, bus drivers are more inclined to choose the strategy of not changing a lane, and social vehicle drivers tend to choose the strategy of not giving way to the bus. The tendencies to choose strategies by both players are consistent with the actual decisions made by drivers. It is proven that the formulated game model is highly reliable to describe bus drivers’ exiting behaviors at bus bay stops and can offer the decent theoretical support for both players to make better decisions. | |
publisher | ASCE | |
title | Game Model of Lane Changing for Bus Exiting at Bus Bay Stops | |
type | Journal Paper | |
journal volume | 147 | |
journal issue | 9 | |
journal title | Journal of Transportation Engineering, Part A: Systems | |
identifier doi | 10.1061/JTEPBS.0000566 | |
journal fristpage | 04021046-1 | |
journal lastpage | 04021046-12 | |
page | 12 | |
tree | Journal of Transportation Engineering, Part A: Systems:;2021:;Volume ( 147 ):;issue: 009 | |
contenttype | Fulltext |