Agent-Based Approach to Travel Demand Modeling



An agent-based travel demand model is developed in which travel demand emerges from the interactions of three types of agents in the transportation system: node, arc, and traveler. Simple local rules of agent behaviors are shown to be capable of efficiently solving complicated transportation problems such as trip distribution and traffic assignment.A unique feature of the agent-based model is that it explicitly models the goal, knowledge, searching behavior, and learning ability of related agents. The proposed model distributes trips from origins to destinations in a disaggregate manner and does not require path enumeration or any standard shortest-path algorithm to assign traffic to the links. A sample 10-by-10 grid network is used to facilitate the presentation. The model is also applied to the Chicago, Illinois, sketch transportation network with nearly 1,000 trip generators and sinks, and possible calibration procedures are discussed. Agent-based modeling techniques provide a flexible travel forecasting framework that facilitates the prediction of important macroscopic travel patterns from microscopic agent behaviors and hence encourages studies on individual travel behaviors. Future research directions are identified, as is the relationship between the agent-based and activity-based approaches for travel forecasting.

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    • "Compared with the conventional models, ABM can better -making behavior and the interactions among them, thus simulating the entire traffic system roundly and dynamically. Previous researchers have conducted some meaningful attempts to implant ABM into demand modeling (Horowitz, 1984; Ben-Akiva et al., 1991; Emmerink et al., 1995; Zhang & Levinson, 2004; Arentze & Timmermans, 2005; Zhang et al, 2011). ABM performs more realistically to human behavioural laws. "
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    ABSTRACT: Transportation systems are large, complex and dynamic. Traditional transportation planning methods are based on 4-step travel demand forecast models, which are not good at describing the dynamic characteristics of transportation systems. On the basis of the existing research, this paper presents the concept of Dynamic Transportation Planning and Operations (DTPO), and explains the connotation and application of DTPO in recurrent and non-recurrent scenarios. In addition, a simulation- based DTPO Approach (SDTPOA) is put forward and a DTPO Platform which integrates travel demand model and traffic simulator is designed. The functions of each module in the DTPO Platform are explained in details as well as the operating mechanism of the platform under both recurrent and non-recurrent situations. The DTPO Platform adopts agent-based travel demand model, which can depict the real world better so as to replace the conventional travel demand models. As the key component of the DTPO Platform, Agent-based Modeling (ABM) is analyzed in this paper.
    Procedia - Social and Behavioral Sciences 11/2013; 96:2332-2343. DOI:10.1016/j.sbspro.2013.08.262
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    • "Unfortunately, the paper does not provide enough details to assess how this highly dynamic activity generation process could be integrated into a larger traffic simulation process; nevertheless , it gives an interesting bottom-up alternative to other highly refined approaches. A non-activity-based approach for travel demand modelling is described in Zhang and Levinson (2004). Their model generates travel demand from interaction between travellers, node, and link agents. "
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    ABSTRACT: In the last few years, the number of papers devoted to applications of agent-based technologies to traffic and transportation engineering has grown enormously. Thus, it seems to be the appropriate time to shed light over the achievements of the last decade, on the questions that have been successfully addressed, as well as on remaining challenging issues. In the present paper, we review the literature related to the areas of agent-based traffic modelling and simulation, and agent-based traffic control and management. Later we discuss and summarize the main achievements and the challenges.
    The Knowledge Engineering Review 06/2013; 29(03):375-403. DOI:10.1017/S0269888913000118 · 0.77 Impact Factor
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    10/2015; 3(4):177-210. DOI:10.3390/systems3040177


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