Solving the berth allocation problem (BAP) in ports is not trivial where the berth resources are limited and various sizes of vessels arrive with dramatically dissimilar loads. Especially in real scenarios, arriving vessels are accepted for a berth with the first come first served (FCFS) priority rule. This study proposes a decision support system coupled with a simulation optimization module based on the swarm-based Artificial Bee Colony optimization algorithm for solving the BAP. The proposed methodology was implemented for the Izmir port in Turkey. To investigate the influences of the vessel priorities on the BAP, four different experimental scenarios based on the single (SQM) and multiple queue models (MQM) were coupled with FCFS and proposed hybrid queue priority (HQP) rule. The results indicated that SQM scenarios were superior to MQM scenarios in a manner of minimizing the average vessel waiting times and the implementation of a dynamic berth allocation strategy for the MQM significantly decreases the vessel waiting times. Results of the SQM also imply that utilization of the HQP approach further minimizes the average vessel waiting times and increases the berth utilization and port throughput without yielding excessive waiting times for the larger vessels compared with the FCFS priority rule.

Container traffic forecasting is important for the operations and the design steps of a seaport facility. In this study, performances of the novel soft computing models were compared for the container traffic forecasting of principal Turkish seaports (Istanbul, Izmir, and Mersin seaports) with excessive container traffic. Four forecasting models were implemented based on Artificial Neural Network with Artificial Bee Colony and Levenberg-Marquardt Algorithms (ANN-ABC and ANN-LM), Multiple Nonlinear Regression with Genetic Algorithm (MNR-GA), and Least Square Support Vector Machine (LSSVM). Forecasts were carried out by using the past records of the gross domestic product, exports, and population of the Turkey as indicators of socioeconomic and demographic status. Performances of the forecasting models were evaluated with several performance metrics. Considering the testing period, the LSSVM, ANN-ABC, and ANN-LM models performed better than the MNR-GA model considering overall fitting and prediction performances of the extreme values in the testing data. The LSSVM model was found to be more reliable compared to the ANN models. Forecasting part of the study suggested that container traffic of the seaports will be increased up to 60%, 67%, and 95% at the 2023 for the Izmir, Mersin, and Istanbul seaports considering official growth scenarios of Turkey.

Poor lane keeping behaviors may cause problems and ‘Level’ of lane keeping has received little attention from the researchers to gain a better understanding on degree of untidiness. Unfortunately, previous studies have not clarified the drivers’ lane keeping levels. For this reason, ‘level of untidy’ should be defined and the question of ‘in which level’ needs to be answered. Paper suggests five levels for quantitative measure of (un)tidiness so that the effects can be assessed for traffic flow models. For this aim, proposed levels are analyzed by considering road, vehicle, and traffic properties in urban roads. An ordered probit regression analysis was performed to investigate relation between keeping level and effective parameters using each vehicle's lateral position data. Results showed that volume and lane width parameters have a positive effect, and vehicle width and lane number variables have negative effect on keeping level as continuous variables of the model.

Passengers’ boarding times at bus stops have a great importance to calculate dwell time and travel time for scheduling process in transport operations. However, there are not so much observed boarding times data in the actual bus transport systems and it causes some prediction problem in scheduling process for public transport operations. For this reason, accurate estimation of the boarding times will supply correct calculation of dwell time and total travel time for bus transit systems. Based on this idea, this article aims to model boarding times of each passengers by evaluating different parameters using two different (statistical and optimization analysis) methods. For this purpose, a comprehensive data collection process was conducted in total seven different cities of Turkey based upon their population. In the study, two new models were developed for the estimation of boarding times by evaluating different effective parameters using a multiple Ordinary Least Square (OLS) regression and Artificial Bee Colony (ABC) algorithm as statistical and optimization methods, respectively. It was found from the analysis that modeling of boarding times by considering various parameters is an effective strategy to improve the performance of bus transport systems. Results of the study also show that model results can be helpful for the researchers and public transport planners wishing to better understand to determine different boarding times of passengers by providing insights on the implementation on dwell time and total travel time calculation for the bus transport system.

Lane-based driving (lane-keeping) problems generally prevail in undeveloped and developing countries, where drivers’ lane-keeping behaviors are fairly weak for reasons such as poor surface conditions, undisciplined driving, low-quality lane line visibility, inappropriate number of lanes and lane width, and nonexistence of raised lane lines with vibration. Drivers’ undisciplined lane-based driving behaviors generally cause an increase in dangerous lateral interactions between vehicles. Thus, problems such as irregular lane utilization, difficulties in traffic management, arbitrary lane changing, and illegal overtaking can be observed on divided multilane urban roads. It is necessary to investigate lane-keeping violations on urban roads to find causal parameters and thereafter formulate suitable solutions. This studied aimed to model lane-based driving behavior by considering the parameters of road geometry (e.g., lane width and number of lanes), vehicle type (passenger car, minibus, light commercial vehicle, midibus, bus, truck, lorry) and traffic flow (volume, heavy vehicle ratio, speed, vehicles in adjacent lanes, and roadside parking) on major urban divided roads in three Turkish cities. For this purpose, lateral position data were collected. A probit regression (PR) analysis was conducted to model the relationship between lane-based driving behavior and the examined parameters. It was found that lane width and lane volume have a positive effect, and heavy vehicle ratio (%), vehicle width, speed, and number of lanes have a negative effect as covariates of the proposed model. Numerical tests also showed the developed PR model to be statistically significant and to have a good ability to fit the observed and predicted data for the examined parameters.

Vertical traffic-calming applications may possess certain disadvantages. For instance, drivers sometimes apply a harsh brake very late or they may even prefer to accelerate rather than slow down to by-pass the suspension's shock threshold level. This has negative consequences on both vehicular and pedestrian traffic. In this study, the effects of chicanes – one of the most common horizontal speed reduction applications – on driver behaviour were examined and certain design characteristics were evaluated in the traffic laboratory of Akdeniz University in Turkey. To complete the driving tests safely and in an economical fashion, they were performed in a driving simulator. The trajectory data of some 125 drivers were saved by the simulator software. To examine these data in detail, a new method was developed using VisualLisp programming. The trajectory data were then transferred to an ArcGIS analysis program and the driving test data on the virtual chicanes were evaluated according to position and speed data. Finally, the optimum chicane design characteristics and their efficiency were determined.