Wild mustard (Brassica juncea L.) oil is evaluated as a feedstock for biodiesel production. Biodiesel was obtained in 94 wt.% yield by a standard transesterification procedure with methanol and sodium methoxide catalyst. Wild mustard oil had a high content of erucic (13(Z)-docosenoic; 45.7 wt.%) acid, with linoleic (9(Z), 12(Z)-octadecadienoic; 14.2 wt.%) and linolenic (9(Z), 12(Z),15(Z)-octadecatrienoic; 13.0 wt.%) acids comprising most of the remaining fatty acid profile. The cetane number, kinematic viscosity, and oxidative stability (Rancimat method) of the methyl esters was 61.1, 5.33 mm 2 s -1 (40°C) and 4.8 h (110°C), respectively. The cloud, pour and cold filter plugging points were 4, -21 and -3°C, respectively. Other properties such as acid value, lubricity, free and total glycerol content, iodine value, Gardner color, specific gravity, as well as sulfur and phosphorous contents were also determined and are discussed in light of biodiesel standards ASTM D6751 and EN 14214. Also reported are the properties and composition of wild mustard oil, along with identification of wild mustard collected in Brazil as Brassica juncea L. (2n = 36) as opposed to the currently accepted Sinapis arvensis L. (2n = 18) classification. In summary, wild mustard oil appears to be an acceptable feedstock for biodiesel production.

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Work zones are a high priority issue in the field of road transportation because of their impacts on traffic safety. A better understanding of work zone crashes can help to identify the contributing factors and countermeasures to enhance roadway safety. This study investigates the prediction of work zone crash severity and the contributing factors by employing a parametric approach using the mixed logit modeling framework and a non-parametric machine learning approach using the support vector machine (SVM). The mixed logit model belongs to the class of random parameter models in which the effects of flexible variables across different observations are identified, that is, data heterogeneity is taken into account. The performance of the SVM model is enhanced by applying three metaheuristic algorithms: particle swarm optimization (PSO), harmony search (HS), and the whale optimization algorithm (WOA). Empirical findings indicate that SVM provides higher prediction accuracy and outperforms the mixed logit model. Estimation results reveal key factors that increase the likelihood of severe work zone crashes. Furthermore, the analysis illustrates the ability of the three metaheuristics to enhance the SVM and the superiority of the harmony search algorithm in improving the performance of the SVM model.

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els are steady-state models, in which traffic demands are assumed to be constant and the input and output flows reach equilibrium (2,10). Further improvement includes providing queue length in small time stamps on the basis of vehicle arrival and departure profiles, first applied in the software TRANSYT (11). This approach was later extended and named the incremental queue accumulation method (12,13). Stochastic analysis is also introduced to address the stochastic and dynamic nature of arterial traffic (10,14). Several recent studies formulate traffic queuing as a Markov chain renewal process (15-18); the queue length is thus estimated on the basis of the condition of previous time steps. The other category of models is based on shock waves of the queue formation and dissipation. These shock wave models can provide detailed temporal and spatial information for the queuing process (6,7,19).Queue length estimation methods leading to practical applications are limited. One of the major difficulties that input-output models encounter is the occurrence of long queues. When the rear of the queue exceeds the advance vehicle detector that provides the arrival traffic volume, the inflow cannot be accurately obtained; the result is large estimation errors (8,9). This limitation is significant because long queues are common on congested arterial links. Although analysis based on shock waves is able to address the problem of long queues (9), detailed information about traffic conditions is required to detect the necessary shock waves; this information is difficult to obtain through existing arterial traffic data collection systems.Recent studies indicate an increasing interest in providing realtime estimates of queue length (3,9,20,21). These studies show the benefit and importance of using new data sources, such as highresolution loop detector data (aggregated in small time intervals or individual vehicle counts) and probe vehicle data. As a new format of probe data, vehicle trajectory data is a topic attracting researchers' interest. Several studies use trajectory data for shock wave detection (22, 23), whereas a few focus on intersection performance. Comert and Cetin (24) studied the conditional probability distribution of the queue length at an isolated intersection given the locations of probe vehicles in the queue. They found that only the location of the last probe in the queue is necessary for queue length estimation. However, the assumption that the actual percentage of probe vehicles among the traffic stream is known limits the application of this method. A simulation study by Shladover and Kuhn (25) investigated the feasibility of using probe trajectories, but it also follows the sampled travel time approach. An impressive study about freeway travel time estimation was conducted by Claudel et al. (26), in which the probe trajectory measurement was converted to density estimation using the Moskowitz function (27, 28). Queue length estimation is an important component of intersection performance measurement. Differ...