An Agent-Based Route Choice Model

Zhu, Shanjiang; Levinson, David; Zhang, Lei

doi:10.2139/ssrn.1743621

Cited by 7 publications

(5 citation statements)

References 26 publications

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“…Nagel and Flötteröd (2009) propose to study traffic assignment from individual agent's perspective, in order to address the complexity of choice dimensions such as the departure time and mode choices. The influence of spatial information on user behaviour is addressed by Zhu et al (2007), who propose an agent-based route choice model to simulate how travellers make route choices over time by using a route choice set. Although the model is able to account for the presence of exogenous information, how such information is propagated among agents is not addressed.…”

Section: Literature Reviewmentioning

confidence: 99%

Agent-based day-to-day traffic network model with information percolation

Shang

Han

Ochieng

et al. 2016

Transportmetrica A: Transport Science

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This paper explores the impact of travel information sharing on road networks using a two-layer, agent-based, day-to-day traffic network model. The first layer (cyber layer) represents a conceptual communication network where travel information is shared among drivers. The second layer (physical layer) captures the day-to-day evolution in a traffic network where individual drivers seek to minimize their own travel costs by making route choices. A key hypothesis in this model is that instead of having perfect information, the drivers form individual groups, among which travel information is shared and utilized for routing decisions. The formation of groups occurs in the cyber layer according to the notion of percolation, which describes the formation of connected clusters (groups) in a random graph. We apply the novel notion of percolation to capture the disaggregated and distributed nature of travel information sharing. We present a numerical study on the convergence of the transport network, when a range of percolation rates are considered. The findings suggest a positive correlation between the percolation rate and the speed of convergence, which is validated through statistical analysis. A sensitivity analysis is also presented which shows a bifurcation phenomenon with regard to certain model parameters.

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Section: Literature Reviewmentioning

confidence: 99%

Agent-based day-to-day traffic network model with information percolation

Shang

Han

Ochieng

et al. 2016

Transportmetrica A: Transport Science

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“…Zhu et al proposed an agent based route choice model where nodes, links and travellers are modelled as agents [18]. The agents communicate with each other to share information and finally, the traveller agents choose their routes.…”

Section: Previous Approaches To Tamentioning

confidence: 99%

“…Researchers have attempted to help drivers minimise their travel times by providing real time traffic information to the drivers [3,9,11,16], as well as by allowing communication with other drivers [18]. Both approaches are based on unrealistic assumptions as using available technology, traffic information will always be incomplete and drivers cannot cooperate with all other parties involved.…”

Section: Introductionmentioning

confidence: 99%

Road traffic optimisation using an evolutionary game

Galib

Moser

2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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In a commuting scenario, drivers expect to arrive at their destinations on time. Drivers have an expectation as to how long it will take to reach the destination. To this end, drivers make independent decisions regarding the routes they take. Independent decision-making is uncoordinated and unlikely to lead to a balanced usage of the road network. However, a well-balanced traffic situation is in the best interest of all drivers, as it minimises their travel times on average over time. This study investigates the possibility of using an Evolutionary Game, Minority Game (MG), to achieve a balanced usage of a road network through independent decisions made by drivers assisted by the MG algorithm. The experimental results show that this simple gametheoretic approach can achieve a near-optimal distribution of traffic in a network. An optimal distribution can be assumed to lead to equitable travel times which are close to the possible minimum considering the number of cars in the network.

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“…Under such condition, activities and travels are generated by means of negotiation. Zhu et al [20] developed an agent-based route choice model to track choices of each individual decision-maker in a road network over time. Jia et al [21] came up with an agent-based traffic assignment framework which enables transportation engineers and planners to evaluate pricing strategies and support their decision-making process by incorporating driver's heterogeneity, route choice and departure time shift model and a Kalman filter learning model.…”

Section: Introductionmentioning

confidence: 99%