Agent-Based Approach to Travel Demand Modeling

J. Trans. Res. Board 09/2007; 1898.


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.

  • Source
    • "One example is the MATSim (Multi-Agent Transport Simulation project (Lammel et al., 2010)) which was initially developed for the simulation of vehicular traffic flow for large cities or even regions (Lammel et al., 2010). Agent-based models are also used in travel demand modeling (Zhang and Levinson, 2004) and freight transport analysis combining with macro-level traffic models Holmgren et al. (2014). TRANSIMS (TRansportation Analysis and SIMulation System) is an activity-based travel demand modeling and simulation tool which was initially developed to simulate individual travelers in a regional transportation network as well as transit system through activity-based travel demand modeling and it can also be used for planning the evacuation of metropolitan areas (Zheng et al., 2013). "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a multimodal evacuation simulation for a near-field tsunami through an agent-based modeling framework in Netlogo. The goals of this paper are to investigate (1) how the varying decisn time impacts the mortality rate, (2) how the choice of different modes of transportation (i.e., walking and automobile), and (3) how existence of vertical evacuation gates impacts the estimation of casualties. Using the city of Seaside, Oregon as a case study site, different individual decision-making time scales are included in the model to assess the mortality rate due to immediate evacuation right after initial earthquake or after a specified milling time. The results show that (1) the decision-making time (τ) and the variations in decision time (σ) are strongly correlated with the mortality rate; (2) the provision of vertical evacuation structures is effective to reduce the mortality rate; (3) the mortality rate is sensitive to the variations in walking speed of the evacuee population; and (4) the higher percentage of automobile use in tsunami evacuation, the higher the mortality rate. Following the results, this paper concludes with a description of the challenges ahead in agent-based tsunami evacuation modeling and simulation, and the modeling of complex interactions between agents (i.e., pedestrian and car interactions) that would arise for a multi-hazard scenario for the Cascadia Subduction Zone.
    Full-text · Article · Dec 2015 · Transportation Research Part C Emerging Technologies
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    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.
    Full-text · Article · Nov 2013 · Procedia - Social and Behavioral Sciences
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    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.
    Full-text · Article · Jun 2013 · The Knowledge Engineering Review
Show more