A Mean-Variance Model for Route Guidance in Advanced Traveler Information Systems. Export

@article{joshi_01, abstract = {Traditional models of route generation are based on choosing routes that minimize expected travel-time between origin and destination. Such approaches do not account for the fact that travelers often incorporate travel-time variability within their decision making. Thus, a route with lower travel-time variability is preferred by some travelers, even if such a route is not one with the lowest mean travel-time. Such traveler behavior is best captured by a multiobjective model in which the choice of a route is based on the mean as well as the variance of the path travel-time. Our route-planning model is intended to help travelers make choices that reflect their decision-making process better. We formulate a network flow multiobjective model in which one of the objectives (expectation) is linear, whereas, the other (variance) is quadratic. In order to present the user with a series of options, we solve a series of parametric 0-1 quadratic integer programs. By utilizing the network structure of the problem, we devise an effective algorithm in which the 0-1 quadratic program is solved by using a continuous relaxation together with an enumeration of some selected paths. Finally, we note that the data requirements for the model can be easily satisfied in practice.}, annote = {This article has two main distinguishing features setting it apart from the other research in the area - it tackles a multiobjective problem and it treats the travel times as random variables. Their main concern it to account for the fact that a small variance may be even more important to commuters than a low mean travel time. In order to introduce the realism of the randomness associated with travel, while not making the model too complicated, they treat the travel times between each node as normal random variables, approximating the mean and variance, easily allowing measurement of the mean and variance of the chosen paths.}, author = {Sen, Suvrajeet and Pillai, Rekha and Joshi, Shirish and Rathi, Ajay K.}, citeulike-article-id = {2292735}, citeulike-linkout-0 = {http://0-search.epnet.com.skyline.cudenver.edu/login.aspx?direct=true&db=aph&an=5649338}, journal = {Transportation Science}, keywords = {bibdesk, multiobjective, network}, local-url = {/Users/ampintye/school/research/network\_flows/joshi\_01.pdf}, month = {Feb}, number = {1}, pages = {37--50}, posted-at = {2008-01-26 16:24:56}, priority = {0}, title = {A Mean-Variance Model for Route Guidance in Advanced Traveler Information Systems.}, url = {http://0-search.epnet.com.skyline.cudenver.edu/login.aspx?direct=true&db=aph&an=5649338}, volume = {35}, year = {2001} }

TY - JOUR ID - joshi_01 L3 - citeulike-article-id:2292735 N2 - Traditional models of route generation are based on choosing routes that minimize expected travel-time between origin and destination. Such approaches do not account for the fact that travelers often incorporate travel-time variability within their decision making. Thus, a route with lower travel-time variability is preferred by some travelers, even if such a route is not one with the lowest mean travel-time. Such traveler behavior is best captured by a multiobjective model in which the choice of a route is based on the mean as well as the variance of the path travel-time. Our route-planning model is intended to help travelers make choices that reflect their decision-making process better. We formulate a network flow multiobjective model in which one of the objectives (expectation) is linear, whereas, the other (variance) is quadratic. In order to present the user with a series of options, we solve a series of parametric 0-1 quadratic integer programs. By utilizing the network structure of the problem, we devise an effective algorithm in which the 0-1 quadratic program is solved by using a continuous relaxation together with an enumeration of some selected paths. Finally, we note that the data requirements for the model can be easily satisfied in practice. IS - 1 JF - Transportation Science EP - 50 TI - A Mean-Variance Model for Route Guidance in Advanced Traveler Information Systems. VL - 35 SP - 37 KW - bibdesk KW - multiobjective KW - network AU - Sen, Suvrajeet AU - Pillai, Rekha AU - Joshi, Shirish AU - Rathi, Ajay PY - 2001/Feb// UR - http://0-search.epnet.com.skyline.cudenver.edu/login.aspx?direct=true&db=aph&an=5649338 ER -