@article{sayyady_fathi_list_stone_2013, title={Locating Traffic Sensors on a Highway Network Models and Algorithms}, ISSN={["2169-4052"]}, DOI={10.3141/2339-04}, abstractNote={ This paper considers the problem of finding optimal sensor locations on a traffic network with the goal of characterizing system use overall. The problem is studied for two practical scenarios. In the first scenario, it is assumed that there is a given number of sensors (p) to be located on the highway network. In this context, the problem is to find a collection of p locations among a given collection of candidate locations. In the second scenario, it is assumed that there is a cost (ci) associated with installing a sensor at each candidate location i and a total budget b. In this context, the problem is to find a collection of locations that provide the best possible characterization given the budget constraint. A metric is proposed for evaluating a potential solution, and then appropriate mathematical models are proposed for solving the problem for each scenario. It is shown that the budget-constrained problem is an extension of the well-known p-median problem. A new Lagrangian heuristic algorithm is presented for solving large instances of this problem when a budget constraint is imposed. A comprehensive computational experiment is used to demonstrate that the Lagrangian heuristic algorithm provides solutions for large-scale networks within reasonable execution times. Examples are based on locating weigh-in-motion sensors on a large-scale highway network. }, number={2339}, journal={TRANSPORTATION RESEARCH RECORD}, author={Sayyady, Fatemeh and Fathi, Yahya and List, George F. and Stone, John R.}, year={2013}, pages={30–38} }
 @article{sayyady_stone_list_jadoun_kim_sajjadi_2011, title={Axle load distribution for mechanistic-empirical pavement design in North Carolina multidimensional clustering approach and decision tree development}, number={2256}, journal={Transportation Research Record}, author={Sayyady, F. and Stone, J. R. and List, G. F. and Jadoun, F. M. and Kim, Y. R. and Sajjadi, S.}, year={2011}, pages={159–168} }
 @article{sayyady_stone_taylor_jadoun_kim_2010, title={Clustering Analysis to Characterize Mechanistic-Empirical Pavement Design Guide Traffic Data in North Carolina}, ISSN={["2169-4052"]}, DOI={10.3141/2160-13}, abstractNote={ This paper presents attempts to generate regional average truck axle load distribution factors (ALFs), monthly adjustment factors (MAFs), hourly distribution factors (HDFs), and vehicle class distributions (VCDs) for North Carolina. The results support Mechanistic–Empirical Pavement Design Guide (MEPDG) procedures. Weigh-in-motion data support the analysis and generate seasonal factors. MEPDG damage-based sensitivity analysis shows that pavement performance is sensitive to North Carolina site-specific ALFs, MAFs, and VCDs. Similar results occur for national default values of ALF, MAF, and VCD. Hierarchical clustering analysis based on North Carolina ALFs and MAFs develops representative seasonal traffic patterns for different regions of the state. Findings show that seasonal truck traffic has distinct characteristics for the eastern coastal plain, the central Piedmont, and the western mountains. A simplified decision tree and a related table help the pavement designer select the proper representative patterns of ALF and MAF. To develop VCD factors, the approach uses 48–h classification counts and a seasonal factoring procedure to account for day-of-week and seasonal variations. The approach incorporates site-specific truck traffic to improve the accuracy of pavement design. On the basis of sensitivity analysis results, pavement performance is found to be insensitive to North Carolina site-specific and national default values of HDF; thus, the average statewide HDF values may be used as input to MEPDG. Specific contributions of this research are the relative insensitivity of pavement performance to HDF, the use of 48-h classification counts to estimate VCD inputs, and a decision tree and table to help pavement designers select the proper ALF and MAF inputs. }, number={2160}, journal={TRANSPORTATION RESEARCH RECORD}, author={Sayyady, Fatemeh and Stone, John R. and Taylor, Kent L. and Jadoun, Fadi M. and Kim, Y. Richard}, year={2010}, pages={118–127} }
 @article{sayyady_eksioglu_2010, title={Optimizing the use of public transit system during no-notice evacuation of urban areas}, volume={59}, ISSN={["1879-0550"]}, DOI={10.1016/j.cie.2010.06.001}, abstractNote={This paper proposes a methodology that can be used to design plans for evacuating transit-dependent citizens during no-notice disasters. A mixed-integer linear program is proposed to model the problem of finding optimal evacuation routes. The objective of the problem is to minimize the total evacuation time and the number of casualties, simultaneously. A traffic simulation package is used to explicitly incorporate the traffic flow dynamics into our model in order to generate solutions which are consistent with the dynamics of traffic network. Due to the long running time of CPLEX, a Tabu search algorithm is designed that finds evacuation routes for transit vehicles. Computational experiments demonstrate that the solutions found are of high-quality. Numerical experiments are conducted using the transportation network of the city of Forth Worth, TX to illustrate the modeling procedure and solution approach.}, number={4}, journal={COMPUTERS & INDUSTRIAL ENGINEERING}, author={Sayyady, Fatemeh and Eksioglu, Sandra D.}, year={2010}, month={Nov}, pages={488–495} }